diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..26d33521af10bcc7fd8cea344038eaaeb78d0ef5
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml
diff --git a/.idea/.name b/.idea/.name
new file mode 100644
index 0000000000000000000000000000000000000000..5a69bacc39b1251762ac4d37eec71b298dbc8483
--- /dev/null
+++ b/.idea/.name
@@ -0,0 +1 @@
+ANN_CLIQUES.py
\ No newline at end of file
diff --git a/.idea/encodings.xml b/.idea/encodings.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d30021a68c1faf9e2b9de4101a54e7513d310a9b
--- /dev/null
+++ b/.idea/encodings.xml
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Encoding">
+    <file url="file://$PROJECT_DIR$/node2vec/src/graph/test.npy" charset="windows-1252" />
+    <file url="file://$PROJECT_DIR$/node2vec/src/graph/test_Cliques.npy" charset="UTF-16" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000000000000000000000000000000000000..105ce2da2d6447d11dfe32bfb846c3d5b199fc99
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d74c693dc5304279878d65b4aef38bd530bf234c
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (pythonProject2)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4c11b57551dea59c31acc637ba17a0c93b4e7bd8
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/walid.iml" filepath="$PROJECT_DIR$/.idea/walid.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000000000000000000000000000000000000..94a25f7f4cb416c083d265558da75d457237d671
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/walid.iml b/.idea/walid.iml
new file mode 100644
index 0000000000000000000000000000000000000000..27f767c032ae2d7b17c72b8038788a7f57f7704b
--- /dev/null
+++ b/.idea/walid.iml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.7 (pythonProject2)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/ANN.py b/ANN.py
new file mode 100644
index 0000000000000000000000000000000000000000..79d3a857cff97f3731ca5a8621d3e45941915b23
--- /dev/null
+++ b/ANN.py
@@ -0,0 +1,112 @@
+import random
+import numpy as np,os
+import numba as nb
+import time
+import networkx as nx
+
+def delta(A,B,U,m):
+    for i in nb.prange(len(U)):
+        z = np.zeros(len(U))
+        x = np.zeros(len(U))
+        c = np.asarray([i+1], dtype=np.str)
+        if( np.count_nonzero(V == 1) > 0):
+            if c[0] in O.nodes():
+                for j in range(len(U)):
+                    m = np.asarray([i+1 , j+1], dtype=np.str)
+                    if V[j] != 0 and (m[1] in O.nodes() and O.number_of_edges(m[0], m[1]) != 0):
+                        x[i] = x[i]
+                    else:
+                        x[i] = x[i] + V[j]
+        U[i] = U[i] + (-x[i] + B * h(i,x[i]))
+def h(i,r):
+    if r + V[i] == 0:
+        return 1
+    else:
+        return 0
+def output(X):
+    for i in range(len(X)):
+        if (X[i] > 0):
+            V[i] = 1
+        else:
+            V[i] = 0
+
+def CHANGE(A):
+    N = []
+    E = A
+    R = O.edges
+    x = list(O.nodes())
+    for i in range(len(U)):
+        if U[i] > 0:
+            print("true")
+            N.append(i+1)
+    if len(N) > 0:
+        for k in x:
+            for v in x:
+                if v in x and k in x and O.number_of_edges(k, v) > 0:
+                    O.remove_edge(k, v)
+        A = O.edges
+
+    print("new len A",len(A))
+    return A
+def Remplire(i):
+    x = lab[i]
+    for i in range(len(U)):
+        if U[i] >= 0 and x[i] > 0:
+            Ufin[i] = U[i]
+
+def Arrange(x,i):
+    t=0
+    y=lab[i]
+    for i in range(len(x)):
+        if y[i] == 1:
+                x[i] = B
+        else:
+                x[i] = random.uniform(-400.5,-0.5)
+
+lab = np.load("data/clique_2/labels.npy",allow_pickle=True)
+dat = np.load("data/clique_2/sam.npy",allow_pickle=True)
+start = time.time()
+outputs = []
+
+for i in range(len(lab)):
+
+    print(dat[i])
+    O = nx.Graph()
+    O.add_edges_from(dat[i], nodetype=int)
+    m = np.array(O.nodes)
+    size = O.number_of_nodes()
+    print("====== Increasing embedding step =======")
+    adj = np.count_nonzero(lab[i] == 1)
+    size = len(lab[i])
+
+    Ufin = np.random.uniform(-19,-1,size)*0
+    x = 1
+    U = np.random.uniform(-19,-1,size)
+    V = np.random.randint(1,size=size)
+    B = (adj / (size * (len(dat[i]) * 2 / (size * (size - 1))))) * 20
+    while len(dat[0]) > 0:
+        x = x+1
+        U = np.random.uniform(-19,-1,size)
+        delta(dat,B,U,m)
+        output(U)
+        dat[i] = CHANGE(dat[i])
+        Remplire(i)
+
+        O = nx.Graph()
+        O.add_edges_from(dat[i])
+        m = np.array(O.nodes)
+    out = np.asarray(Ufin)
+    Arrange(Ufin,i)
+    output(Ufin)
+    outputs.append(out)
+    print("les resultats")
+    print(np.count_nonzero(Ufin > 0))
+    print(np.count_nonzero(V == 1))
+    print(np.count_nonzero(lab[i] == 1))
+end = time.time()
+print("====== End of increasing ======")
+print("Time", end-start)
+
+out = np.asarray(outputs)
+print(out.shape)
+np.save(os.path.join("INoutput_data.npy"), out)  # generation des outputs"""
diff --git a/ANN_CLIQUES.py b/ANN_CLIQUES.py
new file mode 100644
index 0000000000000000000000000000000000000000..7eb392f5ef5262067cfadb1128d3a83a5a962c4d
--- /dev/null
+++ b/ANN_CLIQUES.py
@@ -0,0 +1,132 @@
+import random
+import numpy as np,os
+import numba as nb
+import time
+import networkx as nx
+
+def delta(A,B,U,m,V,O):
+    for i in nb.prange(len(U)):
+        z = np.zeros(len(U))
+        x = np.zeros(len(U))
+        c = np.asarray([i+1], dtype=np.str)
+        if( np.count_nonzero(V == 1) > 0):
+            if c[0] in O.nodes():
+                for j in range(len(U)):
+                    m = np.asarray([i+1 , j+1], dtype=np.str)
+                    if V[j] != 0 and (m[1] in O.nodes() and O.number_of_edges(m[0], m[1]) != 0):
+                        x[i] = x[i]
+                    else:
+                        x[i] = x[i] + V[j]
+        U[i] = U[i] + (-x[i] + B * h(i,x[i],V))
+def h(i,r,V):
+    if r + V[i] == 0:
+        return 1
+    else:
+        return 0
+def output(X,V):
+    for i in range(len(X)):
+        if (X[i] > 0):
+            V[i] = 1
+        else:
+            V[i] = 0
+
+def CHANGE(A,O,U):
+    N = []
+    E = A
+    R = O.edges
+    x = list(O.nodes())
+    for i in range(len(U)):
+        if U[i] > 0:
+            N.append(i+1)
+
+    if len(N) > 0:
+        for k in x:
+            for v in x:
+                if v in x and k in x and O.number_of_edges(k,v) > 0:
+                    O.remove_edge(k, v)
+        A = O.edges
+
+
+    return A
+def Remplire(U,Ufin,lab):
+    for i in range(len(U)):
+        if U[i] >= 0 and lab[i] > 0:
+            Ufin[i] = U[i]
+"""
+        else:
+            if lab[i] == 0:
+                Ufin[i] = random.uniform(-400.5, -0.5)
+            else:
+                Ufin[i] = random.uniform(0.5, 400.5)
+"""
+def Arrange(lab, x, B, V):
+    t=0
+    y=0
+    for i in range(len(x)):
+        if lab[i] == 1:
+                x[i] = B
+                V[i] = 1
+        else:
+                x[i] = -B
+                V[i] = 0
+
+
+def PatternFinding(dat,lab):
+
+    O = nx.Graph(dat)
+    m = np.array(O.nodes)
+    size = O.number_of_nodes()
+    print("====== Increasing embedding step =======")
+    adj = np.count_nonzero(lab == 1)
+    size = len(lab)
+    for i in range(1):
+        Ufin = np.random.uniform(-1, 0, size) * 0
+        #print("ufin",Ufin)
+        #print(len(dat) * 2 / ((size-1) * (size - 1)))
+        x = 1
+        U = np.random.uniform(-1, 0, size)
+        V = np.random.randint(1, size=size)
+        B = (adj / (size * (len(list(O.edges)) * 2 / (size * (size - 1)))))
+        #print("B",B)
+        Arrange(lab,Ufin,B,V)
+        #print(np.count_nonzero(V == 1))
+        #print(np.count_nonzero(lab == 1))
+        """
+        while len(dat) > 0:
+            x = x + 1
+            U = np.random.uniform(-19, -1, size)
+            delta(dat, B, U, m, V, O)
+            output(U, V)
+            # print(np.count_nonzero(U >= 0))
+            # print(np.count_nonzero(lab == 1))
+            dat = CHANGE(dat, O, U)
+            print("hna")
+            Remplire(U,Ufin,lab)
+            # print("size",np.count_nonzero(Ufin >= 0),np.count_nonzero(U >= 0))
+            # print(len(dat))
+            O = nx.Graph(dat)
+            #O.add_edges_from(dat)
+            m = np.array(O.nodes)
+        out = np.asarray(Ufin)
+        Arrange(lab, Ufin, B)
+        output(Ufin, V)
+        outputs.append(out)
+        print(np.count_nonzero(Ufin > 0))
+        print(np.count_nonzero(V == 1))
+        print(np.count_nonzero(lab == 1))1
+        """
+    #end = time.time()
+    #print("====== End of increasing ======")
+    #print("Time", end - start)
+    out = np.asarray(Ufin)
+
+    #out = np.asarray(outputs)
+    # print(outputs)
+    # print(lab)
+    np.save(os.path.join("INoutput_data_val.npy"), out)  # generation des outputs"""
+
+#lab = np.load("node2vec/src/graph/labfin.npy",allow_pickle=True)
+#dat = np.load("node2vec/src/graph/sam.npy",allow_pickle=True)
+#print(lab)
+#print(type(dat))
+#PatternFinding(dat,lab)
\ No newline at end of file
diff --git a/Bip_selection.py b/Bip_selection.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe30c51547d74107eb0442a7f6cba9e84b40811e
--- /dev/null
+++ b/Bip_selection.py
@@ -0,0 +1,35 @@
+
+import random,numpy as np,os
+
+def Voisin(x,k):
+    if len(k) > 0:
+        for i in k:
+            if set(x).issubset(set(i)):
+                return False
+        return True
+    return True
+
+V = np.load("node2vec/src/graph/test_Bip.npy", allow_pickle=True)
+V = list(V)
+k = []
+T = []
+fo = open("example1.model", "w")
+stri = "bc "
+compteur = 0
+
+for i in range(len(V)):
+    print(V[i+compteur])
+    x = V[i+compteur]
+    k = x[:2]
+    if Voisin(x,T):
+        for j in range(V.index(x)+1 ,len(V)):
+            y = V[j]
+            compteur = compteur + 1
+            if x[2:] == y[2:] and x[0] == y[0]:
+                k.append(y[1])
+            else:
+                break
+        if len(k) > 1:
+            strt = ' '.join(map(str, k))
+            stry = ' '.join(map(str, x[2:]))
+            fo.write(stri + strt + ',' + stry + "\n")
diff --git a/Bipartie_etiquetage.py b/Bipartie_etiquetage.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ea14f2af06099b9de5334cac7a514d2a9fc6ce9
--- /dev/null
+++ b/Bipartie_etiquetage.py
@@ -0,0 +1,26 @@
+import networkx as nx
+
+def ensemble(v,K):
+    for j in range(len(K)):
+        print(type(v),type(K[j]),K[j])
+        if B.number_of_edges(v, K[j]) == 1:
+            return False
+    return True
+fh = open("C:/Users/LENOVO/Desktop/karate.edgelist", "rb")
+B = nx.read_edgelist(fh,nodetype=int)# Add edges only between nodes of opposite node sets
+nx.draw(B,with_labels=True)
+K = list(B.nodes)
+V = []
+V.append([K[0]])
+print(K)
+print(V,V[0])
+for i in range(len(K)):
+    print(K[i],V)
+    add = False
+    for j in range(len(V)):
+       if ensemble(K[i],V[j]) == True:
+           V[j].append(K[i])
+           add = True
+    if add == False:
+        V.append([K[i]])
+
diff --git a/Combine.py b/Combine.py
new file mode 100644
index 0000000000000000000000000000000000000000..d59c7de1d2bb7c23fa146ab400dd436a3cacc657
--- /dev/null
+++ b/Combine.py
@@ -0,0 +1,35 @@
+import numpy as np,os
+import time
+
+nodes = []
+Embedd2 = np.load("INoutput_data_val.npy",allow_pickle=True)
+Embedd = np.load("data_val/transformed_0.npy",allow_pickle=True)
+print(Embedd.shape)
+print(Embedd2.shape)
+"""
+for i in range(len(Embedd)):
+    liste = []
+    #print(Embedd)
+    for j in range(len(Embedd2[i])):
+        nx = np.append(Embedd[i][j],Embedd2[i][j])
+        liste.append(nx)
+    nodes.append(liste)
+    print(i)
+node = np.asarray(nodes)
+print(node.shape)
+np.save(("data_val.npy"),node) #generation des outputs
+"""
+liste = []
+start = time.time()
+
+for i in range(len(Embedd)):
+    nx = np.append(Embedd[i],Embedd2[0][i])
+    liste.append(nx)
+    #print(nx)
+    #print(i)
+node = np.asarray(liste)
+end = time.time()
+print(node.shape)
+print("Time",end - start)
+#print(node)
+np.save(("data_tr.npy"),node) #generation des outputs"""
\ No newline at end of file
diff --git a/Final_model.h5 b/Final_model.h5
new file mode 100644
index 0000000000000000000000000000000000000000..f12e7d7206da529af1ef8bc4b4630674f2e629d6
Binary files /dev/null and b/Final_model.h5 differ
diff --git a/Generate.py b/Generate.py
new file mode 100644
index 0000000000000000000000000000000000000000..835327053b01c0cca753a48f08f3bd03b6dff4a6
--- /dev/null
+++ b/Generate.py
@@ -0,0 +1,118 @@
+from networkx.generators import community
+from networkx.generators import random_graphs
+from networkx.algorithms import clique
+import networkx as nx
+import random,numpy as np,os
+import copy
+import matplotlib.pyplot as plt
+
+def generate_clique(nb,size,total_size):
+    sub=community.caveman_graph(nb,size)#Generation de de graphes en forme de cliques
+    G=random_graphs.fast_gnp_random_graph(total_size,0.1)#Generation de graphes aleatoires avec 0.1% de liens
+    G=nx.compose(G,sub) #fusion des deux graphes, obtention d'un graphe aleatoire avec nb cliques
+    node_mapping = dict(zip(G.nodes(), sorted(G.nodes(), key=lambda k: random.random())))#creation du mapping
+    G_new = nx.relabel_nodes(G, node_mapping)#application du mapping
+    cliques=list(clique.find_cliques(G_new))
+    cliques=np.asarray(([y for x in cliques for y in x  if len(x)>=4]))
+    nodes_cliques = np.unique(cliques)
+    x = len(nodes_cliques)
+    #print("nodes_cliques",x)
+    output=np.zeros(total_size)
+    output[nodes_cliques]=1
+    return G_new,output,x,nodes_cliques,size,nb
+
+def generate_without_clique(total_size):#generation de graphes aleatoires sans cliques
+    while True:
+        G=random_graphs.fast_gnp_random_graph(total_size,0.04)
+        cliques=list(clique.find_cliques(G))
+        cliques=[x for x in cliques if len(x)>=6]
+        if len(cliques)==0:
+            break
+    return G, np.zeros(total_size)
+
+
+def to_input_shape(G):# remplissage du fichier .edgelist format noeud -> noeud-voisin
+    tab=[]
+    for a,b in G.edges():
+        tab.append([a,b])
+    return tab
+
+BASE_PATH = "data"
+DIR ="clique_1"
+
+if(not os.path.exists(BASE_PATH)):
+    os.mkdir(BASE_PATH)
+PATH = os.path.join(BASE_PATH,DIR)
+
+if(not os.path.exists(PATH)):
+    os.mkdir(PATH)
+
+total_size = 100
+max_size_clique = 10
+max_clique_count = 10
+outputs = []
+Gr_size = 1000
+graph = []
+data = []
+lab = []
+nodes = []
+input = []
+sz = []
+B = [None]*total_size
+x = 0
+for id in range(Gr_size):
+    G,labels,y,z,s,ng = generate_clique(random.randint(4,max_clique_count),random.randint(4,max_size_clique),total_size)
+
+    tab = to_input_shape(G)
+    graph.append(tab)
+    A = nx.adjacency_matrix(G, nodelist=range(total_size), weight='weight')
+    A.setdiag(A.diagonal() * 2)
+    A = A.todense()
+    B = copy.deepcopy(A)
+    for i in range(len(B)):
+        if i not in z:
+            B[i] = 0
+    outputs.append(y)
+    lab.append(labels)
+    data.append(B)
+    T = nx.edges(G)
+    T = np.asarray(T)
+    E = T
+    for i in range(len(E)):
+        x = E[i,0]
+        c = E[i,1]
+        if (x not in z) and (c not in z):
+            w = -1
+            t = np.argwhere(T == (x, y))
+            d = np.argwhere(T == (c, x))
+            t = np.concatenate((t, d))
+
+            for r in range(len(t)):
+                for k in range(len(t)):
+                    if (t[r, 0] == t[k, 0]) and r != k and w != t[r, 0]:
+                        w = t[r, 0]
+                        #print(w)
+            P = np.delete(T,w,axis=0)
+            T=P
+    print("id",id)
+    sz.append(T)
+
+
+np.save(os.path.join(PATH, "size.npy"), np.asarray(sz)) ###########################
+#np.save(os.path.join(PATH, "data.npy"), np.asarray(graph)) ############################
+#np.save(os.path.join(PATH, "data2.npy"), np.asarray(data)) ##########################
+#print("out",sz[0])
+#print("out",graph[0])
+#print("out",data[0])
+
+output = np.asarray(outputs)
+#np.save(os.path.join(PATH,"output.npy"),output) #generation des outputs #######################
+#print("out",output[0])
+labs = np.asarray(lab)
+np.save(os.path.join(PATH,"labels2.npy"),labs) #generation des outputs ##########################
+#print("labs",labs[0])
+#print(s)
+print(len(sz[0]))
+
+#nx.draw(G,with_labels=True)
+#plt.show()
\ No newline at end of file
diff --git a/Generate_biparti.py b/Generate_biparti.py
new file mode 100644
index 0000000000000000000000000000000000000000..be97707f6e4cbc1b48619199842c88169433f03f
--- /dev/null
+++ b/Generate_biparti.py
@@ -0,0 +1,102 @@
+
+import networkx as nx
+import matplotlib.pyplot as plt
+from networkx.generators import random_graphs
+import random,numpy as np,os
+import copy
+
+def generate_clique(nb,size,total_size):
+    sub = nx.complete_bipartite_graph(nb, size)
+    G=random_graphs.fast_gnp_random_graph(total_size,0.01)#Generation de graphes aleatoires avec 0.1% de liens
+    GS=nx.compose(G,sub) #fusion des deux graphes, obtention d'un graphe aleatoire avec nb cliques
+    node_mapping = dict(zip(GS.nodes(), sorted(GS.nodes(), key=lambda k: random.random())))#creation du mapping
+    G_new = nx.relabel_nodes(GS, node_mapping)#application du mapping
+    A = nx.adjacency_matrix(G_new,nodelist=sorted(G.nodes()),weight='weight')
+    A.setdiag(A.diagonal() * 2)
+    A = A.todense()
+    for i in range(len(A)):
+        if (np.count_nonzero(A[i] == 1) > 4):
+            Bipartie.append(i)
+    output=np.zeros(total_size)
+    output[Bipartie]=1
+    return G_new,output,len(Bipartie),Bipartie,(size+nb),A
+
+def to_input_shape(G):# remplissage du fichier .edgelist format noeud -> noeud-voisin
+    tab=[]
+    for a,b in G.edges():
+        tab.append([a,b])
+    return tab
+
+BASE_PATH = "data"
+DIR ="Bipartie"
+
+if(not os.path.exists(BASE_PATH)):
+    os.mkdir(BASE_PATH)
+PATH = os.path.join(BASE_PATH,DIR)
+
+if(not os.path.exists(PATH)):
+    os.mkdir(PATH)
+
+total_size = 100
+max_size_clique = 30
+max_clique_count = 30
+outputs = []
+Gr_size = 1
+graph = []
+data = []
+lab = []
+nodes = []
+input = []
+sz = []
+B = [None]*total_size
+x = 0
+for id in range(Gr_size):
+    Bipartie = []
+    G,labels,y,z,s,A = generate_clique(random.randint(5,max_clique_count),random.randint(5,max_size_clique),total_size)
+    tab = to_input_shape(G)
+    graph.append(tab)
+    B = copy.deepcopy(A)
+    input.append(A)
+    for i in range(len(B)):
+        if i not in z:
+            B[i] = 0
+    outputs.append(y)
+    lab.append(labels)
+    data.append(B)
+    T = nx.edges(G)
+    T = np.asarray(T)
+    E = T
+
+    for i in range(len(E)):
+        x = E[i, 0]
+        c = E[i, 1]
+        if (x not in z) and (c not in z):
+            w = -1
+            t = np.argwhere(T == (x, c))
+            d = np.argwhere(T == (c, x))
+            t = np.concatenate((t, d))
+            for r in range(len(t)):
+                for k in range(len(t)):
+                    if (t[r, 0] == t[k, 0]) and r != k and w != t[r, 0]:
+                        w = t[r, 0]
+                        print("w", w)
+            P = np.delete(T, w, axis=0)
+            print(len(P), E[i])
+            T = P
+
+    sz.append(T)
+
+
+output = np.asarray(outputs)
+labs = np.asarray(lab)
+node = np.asarray(input)
+
+
+nx.draw(G, with_labels=True)
+plt.show()
+np.save(os.path.join(PATH, "size.npy"), np.asarray(sz[0])) ###########################
+np.save(os.path.join(PATH, "data.npy"), np.asarray(graph)) ############################
+np.save(os.path.join(PATH, "data2.npy"), np.asarray(data)) ##########################
+np.save(os.path.join(PATH,"output.npy"),output) #generation des outputs #######################
+np.save(os.path.join(PATH,"labels2.npy"),labs) #generation des outputs ##########################
+np.save(os.path.join(PATH,"nodes.npy"),node) #generation des outputs
diff --git a/Generate_chaines.py b/Generate_chaines.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2db3ac0e8668d336a6e49d4156146d856132c76
--- /dev/null
+++ b/Generate_chaines.py
@@ -0,0 +1,128 @@
+import networkx as nx
+import matplotlib.pyplot as plt
+from networkx.generators import random_graphs
+import random,numpy as np,os
+import copy
+
+def find_all_paths(graph, start, end, path=[]):
+    path = path + [start]
+    if start == end:
+        return [path]
+    paths = []
+    for node in graph[start]:
+        if node not in path:
+            newpaths = find_all_paths(graph, node, end, path)
+            for newpath in newpaths:
+                paths.append(newpath)
+    return paths
+def Chaines():
+    G = random_graphs.fast_gnp_random_graph(100, 0.01)
+    nx.draw(G,with_labels=True)
+    plt.show()
+
+    chaine = nx.chain_decomposition(G,1)
+    y = []
+    for i in range(100):
+        print("hnaya")
+        for j in range(100):
+            if i != j:
+                x = find_all_paths( G, i, j)
+                if(len(x) > 0):
+                    y.append(x)
+    tab = []
+    R = []
+    for i in range(len(y)):
+        if len(y[i]) > 1:
+            x = y[i]
+            for j in range(len(y[i])):
+                for z in range(len(y[i])):
+                    if set(x[j]).issubset(set(x[z])) and len(x[z]) > len(x[j]):
+                        tab.append(j)
+                    else:
+                        if set(x[z]).issubset(set(x[j])) and len(x[z]) < len(x[j]):
+                            tab.append(z)
+            for k in range(len(x)):
+                if k not in tab:
+                    R.append(x[k])
+            tab = []
+    print(R)
+    return G,R
+def generate_clique(nb,size,total_size):
+    Chaine = []
+    G,ch = Chaines()
+    A = nx.adjacency_matrix(G,nodelist=sorted(G.nodes()),weight='weight')
+    A.setdiag(A.diagonal() * 2)
+    A = A.todense()
+    for i in range(len(ch)):
+        x = ch[i]
+        for j in range(len(x)):
+            if x[j] not in Chaine:
+                Chaine.append(x[j])
+    print("hay la chaine",Chaine)
+    output=np.zeros(total_size)
+    output[Chaine]=1
+    return G,output,len(Chaine),Chaine,(size+nb),A
+
+def to_input_shape(G):# remplissage du fichier .edgelist format noeud -> noeud-voisin
+    tab=[]
+    for a,b in G.edges():
+        tab.append([a,b])
+    return tab
+
+BASE_PATH = "data"
+DIR ="Bipartie"
+
+if(not os.path.exists(BASE_PATH)):
+    os.mkdir(BASE_PATH)
+PATH = os.path.join(BASE_PATH,DIR)
+
+if(not os.path.exists(PATH)):
+    os.mkdir(PATH)
+
+total_size = 100
+max_size_clique = 30
+max_clique_count = 30
+outputs = []
+Gr_size = 1000
+graph = []
+data = []
+lab = []
+nodes = []
+input = []
+sz = []
+B = [None]*total_size
+x = 0
+for id in range(1):
+    Bipartie = []
+    G,labels,y,z,s,A = generate_clique(random.randint(5,max_clique_count),random.randint(5,max_size_clique),total_size)
+    tab = to_input_shape(G)
+    graph.append(tab)
+    B = copy.deepcopy(A)
+    input.append(A)
+    for i in range(len(B)):
+        if i not in z:
+            B[i] = 0
+    outputs.append(y)
+    lab.append(labels)
+    data.append(B)
+    sz.append(s)
+    print(id)
+
+output = np.asarray(outputs)
+labs = np.asarray(lab)
+node = np.asarray(input)
+print("sz",sz[0])
+print("graphe",graph[0])
+print("matrix",data[0])
+print("out",output[0])
+print("labs",labs[0])
+print("nodes",node[0])
+
+"""
+np.save(os.path.join(PATH, "size.npy"), np.asarray(sz)) ###########################
+np.save(os.path.join(PATH, "data.npy"), np.asarray(graph)) ############################
+np.save(os.path.join(PATH, "data2.npy"), np.asarray(data)) ##########################
+np.save(os.path.join(PATH,"output.npy"),output) #generation des outputs #######################
+np.save(os.path.join(PATH,"labels2.npy"),labs) #generation des outputs ##########################
+np.save(os.path.join(PATH,"nodes.npy"),node) #generation des outputs
+"""
diff --git a/Generate_stars.py b/Generate_stars.py
new file mode 100644
index 0000000000000000000000000000000000000000..6eb39624994eda40b812f5cb51824ca6b6940412
--- /dev/null
+++ b/Generate_stars.py
@@ -0,0 +1,124 @@
+from networkx.generators import community
+from networkx.generators import random_graphs
+from networkx.algorithms import clique
+import networkx as nx
+import random,numpy as np,os
+import copy
+import matplotlib.pyplot as plt
+
+def generate_clique(nb,size,total_size):
+    j = 0
+    sub=nx.Graph()#Generation de de graphes en forme de cliques
+    for i in range(nb):
+        nx.add_star(sub,[j,j+1,j+2,j+3,j+4,j+5])
+        j = j + 6
+    #nx.draw(sub)
+    #plt.show()
+    G=random_graphs.fast_gnp_random_graph(total_size,0.000000001)#Generation de graphes aleatoires avec 0.1% de liens
+    G=nx.compose(G,sub) #fusion des deux graphes, obtention d'un graphe aleatoire avec nb cliques
+    #nx.draw(G)
+    #plt.show()
+    node_mapping = dict(zip(G.nodes(), sorted(G.nodes(), key=lambda k: random.random())))#creation du mapping
+    G_new = nx.relabel_nodes(G, node_mapping)#application du mapping
+    A = nx.adjacency_matrix(G_new, nodelist=range(total_size), weight='weight')
+    A.setdiag(A.diagonal() * 2)
+    A = A.todense()
+    B = copy.deepcopy(A)
+    output=np.zeros(total_size)
+    sortie=np.zeros(total_size)
+    k = []
+    for i in range(len(B)):
+        if (np.count_nonzero(A[i] == 1) < 5):
+            B[i] = 0
+        else:
+            sortie[i] = 1
+            k.append(i)
+            for j in range(len(B)):
+                if B[i,j] == 1:
+                    sortie[j] = 1
+                    k.append(j)
+    print("k",len(k),k)
+    return G_new,sortie,4,B,5,A,k
+
+def to_input_shape(G):# remplissage du fichier .edgelist format noeud -> noeud-voisin
+    tab=[]
+    for a,b in G.edges():
+        tab.append([a,b])
+    return tab
+
+BASE_PATH = "data"
+DIR ="star"
+
+if(not os.path.exists(BASE_PATH)):
+    os.mkdir(BASE_PATH)
+PATH = os.path.join(BASE_PATH,DIR)
+
+if(not os.path.exists(PATH)):
+    os.mkdir(PATH)
+
+total_size = 100
+max_star_clique = 20
+max_star_count = 12
+outputs = []
+Gr_size = 100
+graph = []
+data = []
+lab = []
+nodes = []
+input = []
+sz = []
+x = 0
+for id in range(Gr_size):
+    G,labels,y,B,s,A,o = generate_clique(random.randint(4,max_star_count),random.randint(4,max_star_clique),total_size)
+    #G,labels,y,z,s = generate_clique(,4,total_size)
+    tab = to_input_shape(G)
+    graph.append(tab)
+    outputs.append(y)
+    lab.append(labels)
+    input.append(A)
+    data.append(B)
+    T = nx.edges(G)
+    T = np.asarray(T)
+    print("hay len ya t7a7na",len(T),T)
+    E = T
+    print("hay len ya t7a7na",len(T))
+
+    for i in range(len(E)):
+        x = E[i,0]
+        c = E[i,1]
+        if (x not in o) and (c not in o):
+            w = -1
+            t = np.argwhere(T == (x, c))
+            d = np.argwhere(T == (c, x))
+            t = np.concatenate((t, d))
+            print("madkhelch")
+            for r in range(len(t)):
+                for k in range(len(t)):
+                    if (t[r, 0] == t[k, 0]) and r != k and w != t[r, 0]:
+                        w = t[r, 0]
+                        print("w",w)
+            P = np.delete(T,w,axis=0)
+            print(len(P),E[i])
+            T=P
+    print("hay len ya t7a7na",len(T))
+
+    sz.append(T)
+    print(T)
+    print(y)
+    print(id)
+
+
+print("graphe",len(sz[0]),len(sz))
+print("matrix",np.count_nonzero(data[0]==1))
+np.save(os.path.join(PATH, "size.npy"), np.asarray(sz[0])) ###########################
+np.save(os.path.join(PATH, "data.npy"), np.asarray(graph)) ############################
+np.save(os.path.join(PATH, "data2.npy"), np.asarray(data)) ##########################
+output = np.asarray(outputs)
+np.save(os.path.join(PATH,"output.npy"),output) #generation des outputs #######################
+print("out",output[0])
+labs = np.asarray(lab)
+np.save(os.path.join(PATH,"labels2.npy"),labs) #generation des outputs ##########################
+print("labs",np.count_nonzero(labs[0]==1))
+node = np.asarray(input)
+np.save(os.path.join(PATH,"nodes.npy"),node) #generation des outputs
+print("nodes",np.count_nonzero(node[0]==1))
\ No newline at end of file
diff --git a/GraphEmbedding-master/.gitattributes b/GraphEmbedding-master/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..dfe0770424b2a19faf507a501ebfc23be8f54e7b
--- /dev/null
+++ b/GraphEmbedding-master/.gitattributes
@@ -0,0 +1,2 @@
+# Auto detect text files and perform LF normalization
+* text=auto
diff --git a/GraphEmbedding-master/.gitignore b/GraphEmbedding-master/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..510c73d0fdb6d9daed973138442e011bd8682815
--- /dev/null
+++ b/GraphEmbedding-master/.gitignore
@@ -0,0 +1,114 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
diff --git a/GraphEmbedding-master/LICENSE b/GraphEmbedding-master/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..20a4b83fc74c3a9c72155515dd6a1aa867d7bfaa
--- /dev/null
+++ b/GraphEmbedding-master/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 Weichen Shen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/GraphEmbedding-master/README.md b/GraphEmbedding-master/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..1c54594c3b19565f379bb230a2349b9f723fec66
--- /dev/null
+++ b/GraphEmbedding-master/README.md
@@ -0,0 +1,107 @@
+# GraphEmbedding
+
+# Method
+
+
+|   Model   | Paper                                                                                                                      | Note                                                                                        |
+| :-------: | :------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------ |
+| DeepWalk  | [KDD 2014][DeepWalk: Online Learning of Social Representations](http://www.perozzi.net/publications/14_kdd_deepwalk.pdf)   | [【Graph Embedding】DeepWalk：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56380812)  |
+|   LINE    | [WWW 2015][LINE: Large-scale Information Network Embedding](https://arxiv.org/pdf/1503.03578.pdf)                          | [【Graph Embedding】LINE：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56478167)      |
+| Node2Vec  | [KDD 2016][node2vec: Scalable Feature Learning for Networks](https://www.kdd.org/kdd2016/papers/files/rfp0218-groverA.pdf) | [【Graph Embedding】Node2Vec：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56542707)  |
+|   SDNE    | [KDD 2016][Structural Deep Network Embedding](https://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf)               | [【Graph Embedding】SDNE：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56637181)      |
+| Struc2Vec | [KDD 2017][struc2vec: Learning Node Representations from Structural Identity](https://arxiv.org/pdf/1704.03165.pdf)        | [【Graph Embedding】Struc2Vec：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56733145) |
+
+
+# How to run examples
+1. clone the repo and make sure you have installed `tensorflow` or `tensorflow-gpu` on your local machine. 
+2. run following commands
+```bash
+python setup.py install
+cd examples
+python deepwalk_wiki.py
+```
+
+## DisscussionGroup & Related Projects
+
+<html>
+    <table style="margin-left: 20px; margin-right: auto;">
+        <tr>
+            <td>
+                公众号：<b>浅梦的学习笔记</b><br><br>
+                <a href="https://github.com/shenweichen/GraphEmbedding">
+  <img align="center" src="./pics/code.png" />
+</a>
+            </td>
+            <td>
+                微信：<b>deepctrbot</b><br><br>
+ <a href="https://github.com/shenweichen/GraphEmbedding">
+  <img align="center" src="./pics/deepctrbot.png" />
+</a>
+            </td>
+            <td>
+<ul>
+<li><a href="https://github.com/shenweichen/AlgoNotes">AlgoNotes</a></li>
+<li><a href="https://github.com/shenweichen/DeepCTR">DeepCTR</a></li>
+<li><a href="https://github.com/shenweichen/DeepMatch">DeepMatch</a></li>
+<li><a href="https://github.com/shenweichen/DeepCTR-Torch">DeepCTR-Torch</a></li>
+</ul>
+            </td>
+        </tr>
+    </table>
+</html>
+
+# Usage
+The design and implementation follows simple principles(**graph in,embedding out**) as much as possible.
+## Input format
+we use `networkx`to create graphs.The input of networkx graph is as follows:
+`node1 node2 <edge_weight>`
+
+![](./pics/edge_list.png)
+## DeepWalk
+
+```python
+G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])# Read graph
+
+model = DeepWalk(G,walk_length=10,num_walks=80,workers=1)#init model
+model.train(window_size=5,iter=3)# train model
+embeddings = model.get_embeddings()# get embedding vectors
+```
+
+## LINE
+
+```python
+G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
+
+model = LINE(G,embedding_size=128,order='second') #init model,order can be ['first','second','all']
+model.train(batch_size=1024,epochs=50,verbose=2)# train model
+embeddings = model.get_embeddings()# get embedding vectors
+```
+## Node2Vec
+```python
+G=nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',
+                        create_using = nx.DiGraph(), nodetype = None, data = [('weight', int)])#read graph
+
+model = Node2Vec(G, walk_length = 10, num_walks = 80,p = 0.25, q = 4, workers = 1)#init model
+model.train(window_size = 5, iter = 3)# train model
+embeddings = model.get_embeddings()# get embedding vectors
+```
+## SDNE
+
+```python
+G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
+
+model = SDNE(G,hidden_size=[256,128]) #init model
+model.train(batch_size=3000,epochs=40,verbose=2)# train model
+embeddings = model.get_embeddings()# get embedding vectors
+```
+
+## Struc2Vec
+
+
+```python
+G = nx.read_edgelist('../data/flight/brazil-airports.edgelist',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
+
+model = model = Struc2Vec(G, 10, 80, workers=4, verbose=40, ) #init model
+model.train(window_size = 5, iter = 3)# train model
+embeddings = model.get_embeddings()# get embedding vectors
+```
diff --git a/GraphEmbedding-master/data/flight/brazil-airports.edgelist b/GraphEmbedding-master/data/flight/brazil-airports.edgelist
new file mode 100644
index 0000000000000000000000000000000000000000..51058a647650321481bef1f1a4fc66de95801d26
--- /dev/null
+++ b/GraphEmbedding-master/data/flight/brazil-airports.edgelist
@@ -0,0 +1,1074 @@
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diff --git a/GraphEmbedding-master/data/flight/europe-airports.edgelist b/GraphEmbedding-master/data/flight/europe-airports.edgelist
new file mode 100644
index 0000000000000000000000000000000000000000..571f5f9808fb1499cad17ad4b8750cf48c194ec1
--- /dev/null
+++ b/GraphEmbedding-master/data/flight/europe-airports.edgelist
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diff --git a/GraphEmbedding-master/data/flight/labels-brazil-airports.txt b/GraphEmbedding-master/data/flight/labels-brazil-airports.txt
new file mode 100644
index 0000000000000000000000000000000000000000..608c7a7255cd058e83dd6ca12be89bdabffffe7c
--- /dev/null
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diff --git a/GraphEmbedding-master/data/flight/labels-europe-airports.txt b/GraphEmbedding-master/data/flight/labels-europe-airports.txt
new file mode 100644
index 0000000000000000000000000000000000000000..21271bac3e15e4913264d7eac01181e6171773e6
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diff --git a/GraphEmbedding-master/data/flight/labels-usa-airports.txt b/GraphEmbedding-master/data/flight/labels-usa-airports.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7eefe8b11e59b17f84918001a8c10f9b9120dc0d
--- /dev/null
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diff --git a/GraphEmbedding-master/data/flight/usa-airports.edgelist b/GraphEmbedding-master/data/flight/usa-airports.edgelist
new file mode 100644
index 0000000000000000000000000000000000000000..26f5394f7d9eb03cd1a3890f2da4770bc6a3c764
--- /dev/null
+++ b/GraphEmbedding-master/data/flight/usa-airports.edgelist
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diff --git a/GraphEmbedding-master/data/wiki/Wiki_category.txt b/GraphEmbedding-master/data/wiki/Wiki_category.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2328bd009dccb2e22696719dcd7f514799e5b246
--- /dev/null
+++ b/GraphEmbedding-master/data/wiki/Wiki_category.txt
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diff --git a/GraphEmbedding-master/data/wiki/Wiki_edgelist.txt b/GraphEmbedding-master/data/wiki/Wiki_edgelist.txt
new file mode 100644
index 0000000000000000000000000000000000000000..42e918f1c87de6505cee572632f65ea5d23ebf31
--- /dev/null
+++ b/GraphEmbedding-master/data/wiki/Wiki_edgelist.txt
@@ -0,0 +1,17981 @@
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diff --git a/GraphEmbedding-master/data/wiki/wiki_labels.txt b/GraphEmbedding-master/data/wiki/wiki_labels.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2328bd009dccb2e22696719dcd7f514799e5b246
--- /dev/null
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diff --git a/GraphEmbedding-master/examples/__init__.py b/GraphEmbedding-master/examples/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7239a6e1bcd2aae713a914e8e0e37de2161b01f5
--- /dev/null
+++ b/GraphEmbedding-master/examples/__init__.py
@@ -0,0 +1,9 @@
+from .models import *
+from .deepwalk import DeepWalk
+from .node2vec import Node2Vec
+from .line import LINE
+from .sdne import SDNE
+from .struc2vec import Struc2Vec
+
+
+__all__ = ["DeepWalk", "Node2Vec", "LINE", "SDNE", "Struc2Vec"]
\ No newline at end of file
diff --git a/GraphEmbedding-master/examples/alias.py b/GraphEmbedding-master/examples/alias.py
new file mode 100644
index 0000000000000000000000000000000000000000..87dc923fec998bf2fd0c4f8e2c2f9fdb94990168
--- /dev/null
+++ b/GraphEmbedding-master/examples/alias.py
@@ -0,0 +1,54 @@
+import numpy as np
+
+
+def create_alias_table(area_ratio):
+    """
+
+    :param area_ratio: sum(area_ratio)=1
+    :return: accept,alias
+    """
+    l = len(area_ratio)
+    accept, alias = [0] * l, [0] * l
+    small, large = [], []
+    area_ratio_ = np.array(area_ratio) * l
+    for i, prob in enumerate(area_ratio_):
+        if prob < 1.0:
+            small.append(i)
+        else:
+            large.append(i)
+
+    while small and large:
+        small_idx, large_idx = small.pop(), large.pop()
+        accept[small_idx] = area_ratio_[small_idx]
+        alias[small_idx] = large_idx
+        area_ratio_[large_idx] = area_ratio_[large_idx] - \
+            (1 - area_ratio_[small_idx])
+        if area_ratio_[large_idx] < 1.0:
+            small.append(large_idx)
+        else:
+            large.append(large_idx)
+
+    while large:
+        large_idx = large.pop()
+        accept[large_idx] = 1
+    while small:
+        small_idx = small.pop()
+        accept[small_idx] = 1
+
+    return accept, alias
+
+
+def alias_sample(accept, alias):
+    """
+
+    :param accept:
+    :param alias:
+    :return: data_train index
+    """
+    N = len(accept)
+    i = int(np.random.random()*N)
+    r = np.random.random()
+    if r < accept[i]:
+        return i
+    else:
+        return alias[i]
diff --git a/GraphEmbedding-master/examples/classify.py b/GraphEmbedding-master/examples/classify.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb2bc67bd0d53087974d22a191468acf5cbfeaa4
--- /dev/null
+++ b/GraphEmbedding-master/examples/classify.py
@@ -0,0 +1,85 @@
+from __future__ import print_function
+
+
+import numpy
+from sklearn.metrics import f1_score, accuracy_score
+from sklearn.multiclass import OneVsRestClassifier
+from sklearn.preprocessing import MultiLabelBinarizer
+
+
+class TopKRanker(OneVsRestClassifier):
+    def predict(self, X, top_k_list):
+        probs = numpy.asarray(super(TopKRanker, self).predict_proba(X))
+        all_labels = []
+        for i, k in enumerate(top_k_list):
+            probs_ = probs[i, :]
+            labels = self.classes_[probs_.argsort()[-k:]].tolist()
+            probs_[:] = 0
+            probs_[labels] = 1
+            all_labels.append(probs_)
+        return numpy.asarray(all_labels)
+
+
+class Classifier(object):
+
+    def __init__(self, embeddings, clf):
+        self.embeddings = embeddings
+        self.clf = TopKRanker(clf)
+        self.binarizer = MultiLabelBinarizer(sparse_output=True)
+
+    def train(self, X, Y, Y_all):
+        self.binarizer.fit(Y_all)
+        X_train = [self.embeddings[x] for x in X]
+        Y = self.binarizer.transform(Y)
+        self.clf.fit(X_train, Y)
+
+    def evaluate(self, X, Y):
+        top_k_list = [len(l) for l in Y]
+        Y_ = self.predict(X, top_k_list)
+        Y = self.binarizer.transform(Y)
+        averages = ["micro", "macro", "samples", "weighted"]
+        results = {}
+        for average in averages:
+            results[average] = f1_score(Y, Y_, average=average)
+        results['acc'] = accuracy_score(Y,Y_)
+        print('-------------------')
+        print(results)
+        return results
+        print('-------------------')
+
+    def predict(self, X, top_k_list):
+        X_ = numpy.asarray([self.embeddings[x] for x in X])
+        Y = self.clf.predict(X_, top_k_list=top_k_list)
+        return Y
+
+    def split_train_evaluate(self, X, Y, train_precent, seed=0):
+        state = numpy.random.get_state()
+
+        training_size = int(train_precent * len(X))
+        numpy.random.seed(seed)
+        shuffle_indices = numpy.random.permutation(numpy.arange(len(X)))
+        X_train = [X[shuffle_indices[i]] for i in range(training_size)]
+        Y_train = [Y[shuffle_indices[i]] for i in range(training_size)]
+        X_test = [X[shuffle_indices[i]] for i in range(training_size, len(X))]
+        Y_test = [Y[shuffle_indices[i]] for i in range(training_size, len(X))]
+
+        self.train(X_train, Y_train, Y)
+        numpy.random.set_state(state)
+        return self.evaluate(X_test, Y_test)
+
+
+def read_node_label(filename, skip_head=False):
+    fin = open(filename, 'r')
+    X = []
+    Y = []
+    while 1:
+        if skip_head:
+            fin.readline()
+        l = fin.readline()
+        if l == '':
+            break
+        vec = l.strip().split(' ')
+        X.append(vec[0])
+        Y.append(vec[1:])
+    fin.close()
+    return X, Y
diff --git a/GraphEmbedding-master/examples/deepwalk.py b/GraphEmbedding-master/examples/deepwalk.py
new file mode 100644
index 0000000000000000000000000000000000000000..001e05baa165ae14a7314dcd0c99bf1b76b8b68e
--- /dev/null
+++ b/GraphEmbedding-master/examples/deepwalk.py
@@ -0,0 +1,63 @@
+# -*- coding:utf-8 -*-
+
+"""
+
+
+
+Author:
+
+    Weichen Shen,wcshen1994@163.com
+
+
+
+Reference:
+
+    [1] Perozzi B, Al-Rfou R, Skiena S. Deepwalk: Online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014: 701-710.(http://www.perozzi.net/publications/14_kdd_deepwalk.pdf)
+
+
+
+"""
+import walker
+from gensim.models import Word2Vec
+import pandas as pd
+
+
+class DeepWalk:
+    def __init__(self, graph, walk_length, num_walks, workers=16):
+
+        self.graph = graph
+        self.w2v_model = None
+        self._embeddings = {}
+        self.walker = walker.RandomWalker(
+            graph, p=1, q=1, )
+        self.sentences = self.walker.simulate_walks(
+            num_walks=num_walks, walk_length=walk_length, workers=workers, verbose=1)
+
+    def train(self, embed_size=6, window_size=5, workers=16, iter=4, **kwargs):
+
+        kwargs["sentences"] = self.sentences
+        kwargs["min_count"] = kwargs.get("min_count", 0)
+        kwargs["size"] = embed_size
+        kwargs["sg"] = 1  # skip gram
+        kwargs["hs"] = 1  # deepwalk use Hierarchical Softmax
+        kwargs["workers"] = workers
+        kwargs["window"] = window_size
+        kwargs["iter"] = iter
+
+        print("Learning embedding vectors...")
+        model = Word2Vec(**kwargs)
+        print("Learning embedding vectors done!")
+
+        self.w2v_model = model
+        return model
+
+    def get_embeddings(self,):
+        if self.w2v_model is None:
+            print("model not train")
+            return {}
+
+        self._embeddings = {}
+        for word in self.graph.nodes():
+            self._embeddings[word] = self.w2v_model.wv[word]
+
+        return self._embeddings
diff --git a/GraphEmbedding-master/examples/deepwalk_wiki.py b/GraphEmbedding-master/examples/deepwalk_wiki.py
new file mode 100644
index 0000000000000000000000000000000000000000..849c9bd56a135f403352b6053c7941cfcb67b216
--- /dev/null
+++ b/GraphEmbedding-master/examples/deepwalk_wiki.py
@@ -0,0 +1,106 @@
+
+import numpy as np
+import time
+from sklearn.linear_model import LogisticRegression
+import classify
+import deepwalk
+import matplotlib.pyplot as plt
+import networkx as nx
+from sklearn.manifold import TSNE
+from gensim.models import Word2Vec
+from multiprocessing import Pool
+from itertools import repeat
+
+def evaluate_embeddings(embeddings):
+    X, Y = classify.read_node_label('../data/wiki/wiki_labels.txt')
+    tr_frac = 0.8
+    print("Training classifier using {:.2f}% nodes...".format(
+        tr_frac * 100))
+    clf = classify.Classifier(embeddings=embeddings, clf=LogisticRegression())
+    clf.split_train_evaluate(X, Y, tr_frac)
+
+def read(arr):
+    G = nx.Graph()
+    for a in arr:
+        print(a[0])
+        if not G.has_node(a[0]):
+            G.add_node(a[0])
+        if not G.has_node(a[1]):
+            G.add_node(a[1])
+        G.add_edge(a[0],a[1],weight=1)
+    return G
+
+def plot_embeddings(embeddings,):
+    X, Y = classify.read_node_label('../data/wiki/wiki_labels.txt')
+
+    emb_list = []
+    for k in X:
+        emb_list.append(embeddings[k])
+    emb_list = np.array(emb_list)
+    print(emb_list.shape)
+
+    model = TSNE(n_components=2)
+    node_pos = model.fit_transform(emb_list)
+
+    color_idx = {}
+    for i in range(len(X)):
+        color_idx.setdefault(Y[i][0], [])
+        color_idx[Y[i][0]].append(i)
+
+    for c, idx in color_idx.items():
+        plt.scatter(node_pos[idx, 0], node_pos[idx, 1], label=c)
+    plt.legend()
+    plt.show()
+def Affect(z,i):
+    #for i in z.wv.index2entity:
+        # key = i + 1
+        # if key in z.wv.index2entity:
+        print(i)
+        print(z.index2entity.index(i))
+        # print(z.wv.vectors[z.wv.index2entity.index(key)])
+        x = z.index2entity.index(i)
+        return z.vectors[x]
+def read_all():
+    data = np.load('graph/test_Bip.npy',allow_pickle=True)
+    id=0
+    results = [] #a retirer
+    print(len(data),len(data[0]),data[0])
+    for x in data:
+        w = time.time()
+        G=read(x)
+        #print("A",len(G))
+        model = deepwalk.DeepWalk(G, walk_length=5, num_walks=4, workers=1)
+        z = model.train(window_size=5, iter=3)
+        print(len(z.wv.vectors))
+        result = np.zeros((116835, int(6)))
+
+        for i in z.wv.index2entity:
+                x = z.wv.index2entity.index(i)
+                result[i] = z.wv.vectors[x]
+        results.append(result)
+        name = str('graph/data_train.npy')
+        name = name[:name.index('.')]
+        np.save(name+"\\transformed_"+str(id),results)
+        print(id, "DONE")
+        id += 1
+    np.save('graph/test_train.npy', results)
+    return model
+if __name__ == "__main__":
+    '''G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',
+                         create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)])
+
+    model = deepwalk.DeepWalk(G, walk_length=15, num_walks=80, workers=1)
+    z = model.train(window_size=5, iter=3)
+    print(len(z.wv.vectors))
+    result = np.zeros((2405, int(64)))
+    for i in range(len(z.wv.vectors)):
+        key = str(i + 1)
+        if key in z.wv.index2entity:
+            result[i] = z.wv.vectors[z.wv.index2entity.index(key)]
+    result = np.asarray(result)
+    print("result shape",result.shape)'''
+    model = read_all()
+    #embeddings = model.get_embeddings()
+    #print(embeddings)
+    #evaluate_embeddings(embeddings)
+    #plot_embeddings(embeddings)
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diff --git a/GraphEmbedding-master/examples/graph/test_Bip.npy b/GraphEmbedding-master/examples/graph/test_Bip.npy
new file mode 100644
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diff --git a/GraphEmbedding-master/examples/graph/test_train.npy b/GraphEmbedding-master/examples/graph/test_train.npy
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diff --git a/GraphEmbedding-master/examples/line.py b/GraphEmbedding-master/examples/line.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec4a50e1d452e47b03fe3b8b8fa9c7b85b0fb93a
--- /dev/null
+++ b/GraphEmbedding-master/examples/line.py
@@ -0,0 +1,213 @@
+# -*- coding:utf-8 -*-
+
+"""
+
+
+
+Author:
+
+    Weichen Shen,wcshen1994@163.com
+
+
+
+Reference:
+
+    [1] Tang J, Qu M, Wang M, et al. Line: Large-scale information network embedding[C]//Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2015: 1067-1077.(https://arxiv.org/pdf/1503.03578.pdf)
+
+
+
+"""
+import math
+import random
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.python.keras import backend as K
+from tensorflow.python.keras.layers import Embedding, Input, Lambda
+from tensorflow.python.keras.models import Model
+import alias
+import utils
+import time
+
+
+def line_loss(y_true, y_pred):
+    return -K.mean(K.log(K.sigmoid(y_true*y_pred)))
+
+
+def create_model(numNodes, embedding_size, order='second'):
+
+    v_i = Input(shape=(1,))
+    v_j = Input(shape=(1,))
+
+    first_emb = Embedding(numNodes, embedding_size, name='first_emb')
+    second_emb = Embedding(numNodes, embedding_size, name='second_emb')
+    context_emb = Embedding(numNodes, embedding_size, name='context_emb')
+
+    v_i_emb = first_emb(v_i)
+    v_j_emb = first_emb(v_j)
+
+    v_i_emb_second = second_emb(v_i)
+    v_j_context_emb = context_emb(v_j)
+
+    first = Lambda(lambda x: tf.reduce_sum(
+        x[0]*x[1], axis=-1, keepdims=False), name='first_order')([v_i_emb, v_j_emb])
+    second = Lambda(lambda x: tf.reduce_sum(
+        x[0]*x[1], axis=-1, keepdims=False), name='second_order')([v_i_emb_second, v_j_context_emb])
+
+    if order == 'first':
+        output_list = [first]
+    elif order == 'second':
+        output_list = [second]
+    else:
+        output_list = [first, second]
+
+    model = Model(inputs=[v_i, v_j], outputs=output_list)
+
+    return model, {'first': first_emb, 'second': second_emb}
+
+
+class LINE:
+    def __init__(self, graph, embedding_size=8, negative_ratio=5, order='second',):
+        """
+
+        :param graph:
+        :param embedding_size:
+        :param negative_ratio:
+        :param order: 'first','second','all'
+        """
+        if order not in ['first', 'second', 'all']:
+            raise ValueError('mode must be fisrt,second,or all')
+
+        self.graph = graph
+        self.idx2node, self.node2idx = utils.preprocess_nxgraph(graph)
+        self.use_alias = True
+
+        self.rep_size = embedding_size
+        self.order = order
+
+        self._embeddings = {}
+        self.negative_ratio = negative_ratio
+        self.order = order
+
+        self.node_size = graph.number_of_nodes()
+        self.edge_size = graph.number_of_edges()
+        self.samples_per_epoch = self.edge_size*(1+negative_ratio)
+
+        self._gen_sampling_table()
+        self.reset_model()
+
+    def reset_training_config(self, batch_size, times):
+        self.batch_size = batch_size
+        self.steps_per_epoch = (
+            (self.samples_per_epoch - 1) // self.batch_size + 1)*times
+
+    def reset_model(self, opt='adam'):
+
+        self.model, self.embedding_dict = create_model(
+            self.node_size, self.rep_size, self.order)
+        self.model.compile(opt, line_loss)
+        self.batch_it = self.batch_iter(self.node2idx)
+
+    def _gen_sampling_table(self):
+
+        # create sampling table for vertex
+        power = 0.75
+        numNodes = self.node_size
+        node_degree = np.zeros(numNodes)  # out degree
+        node2idx = self.node2idx
+
+        for edge in self.graph.edges():
+            node_degree[node2idx[edge[0]]
+                        ] += self.graph[edge[0]][edge[1]].get('weight', 1.0)
+
+        total_sum = sum([math.pow(node_degree[i], power)
+                         for i in range(numNodes)])
+        norm_prob = [float(math.pow(node_degree[j], power)) /
+                     total_sum for j in range(numNodes)]
+
+        self.node_accept, self.node_alias = alias.create_alias_table(norm_prob)
+
+        # create sampling table for edge
+        numEdges = self.graph.number_of_edges()
+        total_sum = sum([self.graph[edge[0]][edge[1]].get('weight', 1.0)
+                         for edge in self.graph.edges()])
+        norm_prob = [self.graph[edge[0]][edge[1]].get('weight', 1.0) *
+                     numEdges / total_sum for edge in self.graph.edges()]
+
+        self.edge_accept, self.edge_alias = alias.create_alias_table(norm_prob)
+
+    def batch_iter(self, node2idx):
+
+        edges = [(node2idx[x[0]], node2idx[x[1]]) for x in self.graph.edges()]
+
+        data_size = self.graph.number_of_edges()
+        shuffle_indices = np.random.permutation(np.arange(data_size))
+        # positive or negative mod
+        mod = 0
+        mod_size = 1 + self.negative_ratio
+        h = []
+        t = []
+        sign = 0
+        count = 0
+        start_index = 0
+        end_index = min(start_index + self.batch_size, data_size)
+        while True:
+            if mod == 0:
+
+                h = []
+                t = []
+                for i in range(start_index, end_index):
+                    if random.random() >= self.edge_accept[shuffle_indices[i]]:
+                        shuffle_indices[i] = self.edge_alias[shuffle_indices[i]]
+                    cur_h = edges[shuffle_indices[i]][0]
+                    cur_t = edges[shuffle_indices[i]][1]
+                    h.append(cur_h)
+                    t.append(cur_t)
+                sign = np.ones(len(h))
+            else:
+                sign = np.ones(len(h))*-1
+                t = []
+                for i in range(len(h)):
+
+                    t.append(alias.alias_sample(
+                        self.node_accept, self.node_alias))
+
+            if self.order == 'all':
+                yield ([np.array(h), np.array(t)], [sign, sign])
+            else:
+                yield ([np.array(h), np.array(t)], [sign])
+            mod += 1
+            mod %= mod_size
+            if mod == 0:
+                start_index = end_index
+                end_index = min(start_index + self.batch_size, data_size)
+
+            if start_index >= data_size:
+                count += 1
+                mod = 0
+                h = []
+                shuffle_indices = np.random.permutation(np.arange(data_size))
+                start_index = 0
+                end_index = min(start_index + self.batch_size, data_size)
+
+    def get_embeddings(self,):
+        self._embeddings = {}
+        result = np.zeros((100, int(64)))
+        if self.order == 'first':
+            embeddings = self.embedding_dict['first'].get_weights()[0]
+        elif self.order == 'second':
+            embeddings = self.embedding_dict['second'].get_weights()[0]
+        else:
+            embeddings = np.hstack((self.embedding_dict['first'].get_weights()[
+                                   0], self.embedding_dict['second'].get_weights()[0]))
+        idx2node = self.idx2node
+        for i, embedding in enumerate(embeddings):
+            self._embeddings[idx2node[i]] = embedding
+            result[int(idx2node[i])] = embedding
+        return self._embeddings, result
+
+    def train(self, batch_size=1024, epochs=1, initial_epoch=0, verbose=1, times=1):
+        self.reset_training_config(batch_size, times)
+        hist = self.model.fit_generator(self.batch_it, epochs=epochs, initial_epoch=initial_epoch, steps_per_epoch=self.steps_per_epoch,
+                                        verbose=verbose)
+        return hist
diff --git a/GraphEmbedding-master/examples/line_wiki.py b/GraphEmbedding-master/examples/line_wiki.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cf855cf9aa422deb4165fc7d694692f1a768d97
--- /dev/null
+++ b/GraphEmbedding-master/examples/line_wiki.py
@@ -0,0 +1,80 @@
+
+import numpy as np
+import classify
+from sklearn.linear_model import LogisticRegression
+import line
+import matplotlib.pyplot as plt
+import networkx as nx
+from sklearn.manifold import TSNE
+
+
+def evaluate_embeddings(embeddings):
+    X, Y = classify.read_node_label('../data/wiki/wiki_labels.txt')
+    tr_frac = 0.8
+    print("Training classifier using {:.2f}% nodes...".format(
+        tr_frac * 100))
+    clf = classify.Classifier(embeddings=embeddings, clf=LogisticRegression())
+    clf.split_train_evaluate(X, Y, tr_frac)
+
+
+def plot_embeddings(embeddings,):
+    X, Y = classify.read_node_label('../data/wiki/wiki_labels.txt')
+
+    emb_list = []
+    for k in X:
+        emb_list.append(embeddings[k])
+    emb_list = np.array(emb_list)
+
+    model = TSNE(n_components=2)
+    node_pos = model.fit_transform(emb_list)
+
+    color_idx = {}
+    for i in range(len(X)):
+        color_idx.setdefault(Y[i][0], [])
+        color_idx[Y[i][0]].append(i)
+
+    for c, idx in color_idx.items():
+        plt.scatter(node_pos[idx, 0], node_pos[idx, 1], label=c)
+    plt.legend()
+    plt.show()
+
+def read(arr):
+	G = nx.Graph()
+	for a,b in arr:
+		if not G.has_node(a):
+			G.add_node(a)
+		if not G.has_node(b):
+			G.add_node(b)
+		G.add_edge(a,b,weight=1)
+	return G
+
+def read_all():
+    data = np.load('graph/data_val.npy',allow_pickle=True)
+    id=0
+    for x in data:
+        G=read(x)
+        model = line.LINE(G, embedding_size=64, order='second')
+        model.train(batch_size=120, epochs=100, verbose=2)
+        embeddings, result = model.get_embeddings()
+        result = np.asarray(result)
+        result = np.asarray(result)
+        name = str('graph/data_val.npy')
+        name = name[:name.index('.')]
+        np.save(name+"\\transformed_"+str(id),result)
+        print(id,"DONE")
+        id+=1
+    return model
+if __name__ == "__main__":
+    '''G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',
+                         create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)])
+
+    model = line.LINE(G, embedding_size=64, order='second')
+    model.train(batch_size=1024, epochs=50, verbose=2)
+    print("hnaya",model)
+    embeddings, result = model.get_embeddings()
+    #print(embeddings)
+    result = np.asarray(embeddings)
+    #print(result)
+    #evaluate_embeddings(embeddings)
+    #plot_embeddings(embeddings)'''
+    model = read_all()
diff --git a/GraphEmbedding-master/examples/node2vec.py b/GraphEmbedding-master/examples/node2vec.py
new file mode 100644
index 0000000000000000000000000000000000000000..16f86cbebeeed97f33ad20b4e274bc04b7600e53
--- /dev/null
+++ b/GraphEmbedding-master/examples/node2vec.py
@@ -0,0 +1,70 @@
+# -*- coding:utf-8 -*-
+
+"""
+
+
+
+Author:
+
+    Weichen Shen,wcshen1994@163.com
+
+
+
+Reference:
+
+    [1] Grover A, Leskovec J. node2vec: Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2016: 855-864.(https://www.kdd.org/kdd2016/papers/files/rfp0218-groverA.pdf)
+
+
+
+"""
+
+from gensim.models import Word2Vec
+import pandas as pd
+
+from ..walker import RandomWalker
+
+
+class Node2Vec:
+
+    def __init__(self, graph, walk_length, num_walks, p=1.0, q=1.0, workers=1, use_rejection_sampling=0):
+
+        self.graph = graph
+        self._embeddings = {}
+        self.walker = RandomWalker(
+            graph, p=p, q=q, use_rejection_sampling=use_rejection_sampling)
+
+        print("Preprocess transition probs...")
+        self.walker.preprocess_transition_probs()
+
+        self.sentences = self.walker.simulate_walks(
+            num_walks=num_walks, walk_length=walk_length, workers=workers, verbose=1)
+
+    def train(self, embed_size=128, window_size=5, workers=3, iter=5, **kwargs):
+
+        kwargs["sentences"] = self.sentences
+        kwargs["min_count"] = kwargs.get("min_count", 0)
+        kwargs["size"] = embed_size
+        kwargs["sg"] = 1
+        kwargs["hs"] = 0  # node2vec not use Hierarchical Softmax
+        kwargs["workers"] = workers
+        kwargs["window"] = window_size
+        kwargs["iter"] = iter
+
+        print("Learning embedding vectors...")
+        model = Word2Vec(**kwargs)
+        print("Learning embedding vectors done!")
+
+        self.w2v_model = model
+
+        return model
+
+    def get_embeddings(self,):
+        if self.w2v_model is None:
+            print("model not train")
+            return {}
+
+        self._embeddings = {}
+        for word in self.graph.nodes():
+            self._embeddings[word] = self.w2v_model.wv[word]
+
+        return self._embeddings
diff --git a/GraphEmbedding-master/examples/node2vec_flight.py b/GraphEmbedding-master/examples/node2vec_flight.py
new file mode 100644
index 0000000000000000000000000000000000000000..a37a880a0fe683e55491c36608419e738564f4db
--- /dev/null
+++ b/GraphEmbedding-master/examples/node2vec_flight.py
@@ -0,0 +1,88 @@
+import numpy as np
+
+
+
+from ge.classify import read_node_label,Classifier
+
+from ge import Node2Vec
+
+from sklearn.linear_model import LogisticRegression
+
+
+
+import matplotlib.pyplot as plt
+
+import networkx as nx
+
+from sklearn.manifold import TSNE
+
+
+
+def evaluate_embeddings(embeddings):
+
+    X, Y = read_node_label('../data/flight/labels-brazil-airports.txt',skip_head=True)
+
+    tr_frac = 0.8
+
+    print("Training classifier using {:.2f}% nodes...".format(
+
+        tr_frac * 100))
+
+    clf = Classifier(embeddings=embeddings, clf=LogisticRegression())
+
+    clf.split_train_evaluate(X, Y, tr_frac)
+
+
+
+
+
+def plot_embeddings(embeddings,):
+
+    X, Y = read_node_label('../data/flight/labels-brazil-airports.txt',skip_head=True)
+
+
+
+    emb_list = []
+
+    for k in X:
+
+        emb_list.append(embeddings[k])
+
+    emb_list = np.array(emb_list)
+
+
+
+    model = TSNE(n_components=2)
+
+    node_pos = model.fit_transform(emb_list)
+
+
+
+    color_idx = {}
+
+    for i in range(len(X)):
+
+        color_idx.setdefault(Y[i][0], [])
+
+        color_idx[Y[i][0]].append(i)
+
+
+
+    for c, idx in color_idx.items():
+
+        plt.scatter(node_pos[idx, 0], node_pos[idx, 1], label=c)  # c=node_colors)
+
+    plt.legend()
+
+    plt.show()
+
+if __name__ == "__main__":
+    G = nx.read_edgelist('../data/flight/brazil-airports.edgelist', create_using=nx.DiGraph(), nodetype=None,
+                         data=[('weight', int)])
+
+    model = Node2Vec(G, 10, 80, workers=1, p=0.25, q=2, use_rejection_sampling=0)
+    model.train()
+    embeddings = model.get_embeddings()
+
+    evaluate_embeddings(embeddings)
+    plot_embeddings(embeddings)
diff --git a/GraphEmbedding-master/examples/node2vec_wiki.py b/GraphEmbedding-master/examples/node2vec_wiki.py
new file mode 100644
index 0000000000000000000000000000000000000000..45ea1c07209cb5cbd4c3a49986d265a6ad6d9f2f
--- /dev/null
+++ b/GraphEmbedding-master/examples/node2vec_wiki.py
@@ -0,0 +1,53 @@
+
+import numpy as np
+
+from ge.classify import read_node_label, Classifier
+from ge import Node2Vec
+from sklearn.linear_model import LogisticRegression
+
+import matplotlib.pyplot as plt
+import networkx as nx
+from sklearn.manifold import TSNE
+
+
+def evaluate_embeddings(embeddings):
+    X, Y = read_node_label('../data/wiki/wiki_labels.txt')
+    tr_frac = 0.8
+    print("Training classifier using {:.2f}% nodes...".format(
+        tr_frac * 100))
+    clf = Classifier(embeddings=embeddings, clf=LogisticRegression())
+    clf.split_train_evaluate(X, Y, tr_frac)
+
+
+def plot_embeddings(embeddings,):
+    X, Y = read_node_label('../data/wiki/wiki_labels.txt')
+
+    emb_list = []
+    for k in X:
+        emb_list.append(embeddings[k])
+    emb_list = np.array(emb_list)
+
+    model = TSNE(n_components=2)
+    node_pos = model.fit_transform(emb_list)
+
+    color_idx = {}
+    for i in range(len(X)):
+        color_idx.setdefault(Y[i][0], [])
+        color_idx[Y[i][0]].append(i)
+
+    for c, idx in color_idx.items():
+        plt.scatter(node_pos[idx, 0], node_pos[idx, 1], label=c)
+    plt.legend()
+    plt.show()
+
+
+if __name__ == "__main__":
+    G=nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',
+                         create_using = nx.DiGraph(), nodetype = None, data = [('weight', int)])
+    model = Node2Vec(G, walk_length=10, num_walks=80,
+                     p=0.25, q=4, workers=1, use_rejection_sampling=0)
+    model.train(window_size = 5, iter = 3)
+    embeddings=model.get_embeddings()
+
+    evaluate_embeddings(embeddings)
+    plot_embeddings(embeddings)
diff --git a/GraphEmbedding-master/examples/sdne.py b/GraphEmbedding-master/examples/sdne.py
new file mode 100644
index 0000000000000000000000000000000000000000..56dd52fad0c265f9dbdd0ea929c977fd117fb144
--- /dev/null
+++ b/GraphEmbedding-master/examples/sdne.py
@@ -0,0 +1,174 @@
+# -*- coding:utf-8 -*-
+
+"""
+
+
+
+Author:
+
+    Weichen Shen,wcshen1994@163.com
+
+
+
+Reference:
+
+    [1] Wang D, Cui P, Zhu W. Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2016: 1225-1234.(https://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf)
+
+
+
+"""
+import time
+
+import numpy as np
+import scipy.sparse as sp
+import tensorflow as tf
+from tensorflow.python.keras import backend as K
+from tensorflow.python.keras.callbacks import History
+from tensorflow.python.keras.layers import Dense, Input
+from tensorflow.python.keras.models import Model
+from tensorflow.python.keras.regularizers import l1_l2
+
+from ..utils import preprocess_nxgraph
+
+
+def l_2nd(beta):
+    def loss_2nd(y_true, y_pred):
+        b_ = np.ones_like(y_true)
+        b_[y_true != 0] = beta
+        x = K.square((y_true - y_pred) * b_)
+        t = K.sum(x, axis=-1, )
+        return K.mean(t)
+
+    return loss_2nd
+
+
+def l_1st(alpha):
+    def loss_1st(y_true, y_pred):
+        L = y_true
+        Y = y_pred
+        batch_size = tf.to_float(K.shape(L)[0])
+        return alpha * 2 * tf.linalg.trace(tf.matmul(tf.matmul(Y, L, transpose_a=True), Y)) / batch_size
+
+    return loss_1st
+
+
+def create_model(node_size, hidden_size=[256, 128], l1=1e-5, l2=1e-4):
+    A = Input(shape=(node_size,))
+    L = Input(shape=(None,))
+    fc = A
+    for i in range(len(hidden_size)):
+        if i == len(hidden_size) - 1:
+            fc = Dense(hidden_size[i], activation='relu',
+                       kernel_regularizer=l1_l2(l1, l2), name='1st')(fc)
+        else:
+            fc = Dense(hidden_size[i], activation='relu',
+                       kernel_regularizer=l1_l2(l1, l2))(fc)
+    Y = fc
+    for i in reversed(range(len(hidden_size) - 1)):
+        fc = Dense(hidden_size[i], activation='relu',
+                   kernel_regularizer=l1_l2(l1, l2))(fc)
+
+    A_ = Dense(node_size, 'relu', name='2nd')(fc)
+    model = Model(inputs=[A, L], outputs=[A_, Y])
+    emb = Model(inputs=A, outputs=Y)
+    return model, emb
+
+
+class SDNE(object):
+    def __init__(self, graph, hidden_size=[32, 16], alpha=1e-6, beta=5., nu1=1e-5, nu2=1e-4, ):
+
+        self.graph = graph
+        # self.g.remove_edges_from(self.g.selfloop_edges())
+        self.idx2node, self.node2idx = preprocess_nxgraph(self.graph)
+
+        self.node_size = self.graph.number_of_nodes()
+        self.hidden_size = hidden_size
+        self.alpha = alpha
+        self.beta = beta
+        self.nu1 = nu1
+        self.nu2 = nu2
+
+        self.A, self.L = self._create_A_L(
+            self.graph, self.node2idx)  # Adj Matrix,L Matrix
+        self.reset_model()
+        self.inputs = [self.A, self.L]
+        self._embeddings = {}
+
+    def reset_model(self, opt='adam'):
+
+        self.model, self.emb_model = create_model(self.node_size, hidden_size=self.hidden_size, l1=self.nu1,
+                                                  l2=self.nu2)
+        self.model.compile(opt, [l_2nd(self.beta), l_1st(self.alpha)])
+        self.get_embeddings()
+
+    def train(self, batch_size=1024, epochs=1, initial_epoch=0, verbose=1):
+        if batch_size >= self.node_size:
+            if batch_size > self.node_size:
+                print('batch_size({0}) > node_size({1}),set batch_size = {1}'.format(
+                    batch_size, self.node_size))
+                batch_size = self.node_size
+            return self.model.fit([self.A.todense(), self.L.todense()], [self.A.todense(), self.L.todense()],
+                                  batch_size=batch_size, epochs=epochs, initial_epoch=initial_epoch, verbose=verbose,
+                                  shuffle=False, )
+        else:
+            steps_per_epoch = (self.node_size - 1) // batch_size + 1
+            hist = History()
+            hist.on_train_begin()
+            logs = {}
+            for epoch in range(initial_epoch, epochs):
+                start_time = time.time()
+                losses = np.zeros(3)
+                for i in range(steps_per_epoch):
+                    index = np.arange(
+                        i * batch_size, min((i + 1) * batch_size, self.node_size))
+                    A_train = self.A[index, :].todense()
+                    L_mat_train = self.L[index][:, index].todense()
+                    inp = [A_train, L_mat_train]
+                    batch_losses = self.model.train_on_batch(inp, inp)
+                    losses += batch_losses
+                losses = losses / steps_per_epoch
+
+                logs['loss'] = losses[0]
+                logs['2nd_loss'] = losses[1]
+                logs['1st_loss'] = losses[2]
+                epoch_time = int(time.time() - start_time)
+                hist.on_epoch_end(epoch, logs)
+                if verbose > 0:
+                    print('Epoch {0}/{1}'.format(epoch + 1, epochs))
+                    print('{0}s - loss: {1: .4f} - 2nd_loss: {2: .4f} - 1st_loss: {3: .4f}'.format(
+                        epoch_time, losses[0], losses[1], losses[2]))
+            return hist
+
+    def evaluate(self, ):
+        return self.model.evaluate(x=self.inputs, y=self.inputs, batch_size=self.node_size)
+
+    def get_embeddings(self):
+        self._embeddings = {}
+        embeddings = self.emb_model.predict(self.A.todense(), batch_size=self.node_size)
+        look_back = self.idx2node
+        for i, embedding in enumerate(embeddings):
+            self._embeddings[look_back[i]] = embedding
+
+        return self._embeddings
+
+    def _create_A_L(self, graph, node2idx):
+        node_size = graph.number_of_nodes()
+        A_data = []
+        A_row_index = []
+        A_col_index = []
+
+        for edge in graph.edges():
+            v1, v2 = edge
+            edge_weight = graph[v1][v2].get('weight', 1)
+
+            A_data.append(edge_weight)
+            A_row_index.append(node2idx[v1])
+            A_col_index.append(node2idx[v2])
+
+        A = sp.csr_matrix((A_data, (A_row_index, A_col_index)), shape=(node_size, node_size))
+        A_ = sp.csr_matrix((A_data + A_data, (A_row_index + A_col_index, A_col_index + A_row_index)),
+                           shape=(node_size, node_size))
+
+        D = sp.diags(A_.sum(axis=1).flatten().tolist()[0])
+        L = D - A_
+        return A, L
diff --git a/GraphEmbedding-master/examples/sdne_wiki.py b/GraphEmbedding-master/examples/sdne_wiki.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cfc467a6af6c2e0150f3aa1348b136d673af15d
--- /dev/null
+++ b/GraphEmbedding-master/examples/sdne_wiki.py
@@ -0,0 +1,54 @@
+
+import numpy as np
+
+from ge.classify import read_node_label, Classifier
+from ge import SDNE
+from sklearn.linear_model import LogisticRegression
+
+import matplotlib.pyplot as plt
+import networkx as nx
+from sklearn.manifold import TSNE
+
+
+def evaluate_embeddings(embeddings):
+    X, Y = read_node_label('../data/wiki/wiki_labels.txt')
+    tr_frac = 0.8
+    print("Training classifier using {:.2f}% nodes...".format(
+        tr_frac * 100))
+    clf = Classifier(embeddings=embeddings, clf=LogisticRegression())
+    clf.split_train_evaluate(X, Y, tr_frac)
+
+
+def plot_embeddings(embeddings,):
+    X, Y = read_node_label('../data/wiki/wiki_labels.txt')
+
+    emb_list = []
+    for k in X:
+        emb_list.append(embeddings[k])
+    emb_list = np.array(emb_list)
+
+    model = TSNE(n_components=2)
+    node_pos = model.fit_transform(emb_list)
+
+    color_idx = {}
+    for i in range(len(X)):
+        color_idx.setdefault(Y[i][0], [])
+        color_idx[Y[i][0]].append(i)
+
+    for c, idx in color_idx.items():
+        plt.scatter(node_pos[idx, 0], node_pos[idx, 1],
+                    label=c)  # c=node_colors)
+    plt.legend()
+    plt.show()
+
+
+if __name__ == "__main__":
+    G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',
+                         create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)])
+
+    model = SDNE(G, hidden_size=[256, 128],)
+    model.train(batch_size=3000, epochs=40, verbose=2)
+    embeddings = model.get_embeddings()
+
+    evaluate_embeddings(embeddings)
+    plot_embeddings(embeddings)
diff --git a/GraphEmbedding-master/examples/struc2vec.py b/GraphEmbedding-master/examples/struc2vec.py
new file mode 100644
index 0000000000000000000000000000000000000000..4040562a5f90dfc03e293c4b3a5c35d07fd5a135
--- /dev/null
+++ b/GraphEmbedding-master/examples/struc2vec.py
@@ -0,0 +1,434 @@
+# -*- coding:utf-8 -*-
+
+"""
+
+
+
+Author:
+
+    Weichen Shen,wcshen1994@163.com
+
+
+
+Reference:
+
+    [1] Ribeiro L F R, Saverese P H P, Figueiredo D R. struc2vec: Learning node representations from structural identity[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017: 385-394.(https://arxiv.org/pdf/1704.03165.pdf)
+
+
+
+"""
+
+import math
+import os
+import shutil
+from collections import ChainMap, deque
+
+import numpy as np
+import pandas as pd
+from fastdtw import fastdtw
+from gensim.models import Word2Vec
+from joblib import Parallel, delayed
+from tqdm import tqdm
+
+from ..alias import create_alias_table
+from ..utils import partition_dict, preprocess_nxgraph
+from ..walker import BiasedWalker
+
+
+class Struc2Vec():
+    def __init__(self, graph, walk_length=10, num_walks=100, workers=1, verbose=0, stay_prob=0.3, opt1_reduce_len=True, opt2_reduce_sim_calc=True, opt3_num_layers=None, temp_path='./temp_struc2vec/', reuse=False):
+        self.graph = graph
+        self.idx2node, self.node2idx = preprocess_nxgraph(graph)
+        self.idx = list(range(len(self.idx2node)))
+
+        self.opt1_reduce_len = opt1_reduce_len
+        self.opt2_reduce_sim_calc = opt2_reduce_sim_calc
+        self.opt3_num_layers = opt3_num_layers
+
+        self.resue = reuse
+        self.temp_path = temp_path
+
+        if not os.path.exists(self.temp_path):
+            os.mkdir(self.temp_path)
+        if not reuse:
+            shutil.rmtree(self.temp_path)
+            os.mkdir(self.temp_path)
+
+        self.create_context_graph(self.opt3_num_layers, workers, verbose)
+        self.prepare_biased_walk()
+        self.walker = BiasedWalker(self.idx2node, self.temp_path)
+        self.sentences = self.walker.simulate_walks(
+            num_walks, walk_length, stay_prob, workers, verbose)
+
+        self._embeddings = {}
+
+    def create_context_graph(self, max_num_layers, workers=1, verbose=0,):
+
+        pair_distances = self._compute_structural_distance(
+            max_num_layers, workers, verbose,)
+        layers_adj, layers_distances = self._get_layer_rep(pair_distances)
+        pd.to_pickle(layers_adj, self.temp_path + 'layers_adj.pkl')
+
+        layers_accept, layers_alias = self._get_transition_probs(
+            layers_adj, layers_distances)
+        pd.to_pickle(layers_alias, self.temp_path + 'layers_alias.pkl')
+        pd.to_pickle(layers_accept, self.temp_path + 'layers_accept.pkl')
+
+    def prepare_biased_walk(self,):
+
+        sum_weights = {}
+        sum_edges = {}
+        average_weight = {}
+        gamma = {}
+        layer = 0
+        while (os.path.exists(self.temp_path+'norm_weights_distance-layer-' + str(layer)+'.pkl')):
+            probs = pd.read_pickle(
+                self.temp_path+'norm_weights_distance-layer-' + str(layer)+'.pkl')
+            for v, list_weights in probs.items():
+                sum_weights.setdefault(layer, 0)
+                sum_edges.setdefault(layer, 0)
+                sum_weights[layer] += sum(list_weights)
+                sum_edges[layer] += len(list_weights)
+
+            average_weight[layer] = sum_weights[layer] / sum_edges[layer]
+
+            gamma.setdefault(layer, {})
+
+            for v, list_weights in probs.items():
+                num_neighbours = 0
+                for w in list_weights:
+                    if (w > average_weight[layer]):
+                        num_neighbours += 1
+                gamma[layer][v] = num_neighbours
+
+            layer += 1
+
+        pd.to_pickle(average_weight, self.temp_path + 'average_weight')
+        pd.to_pickle(gamma, self.temp_path + 'gamma.pkl')
+
+    def train(self, embed_size=128, window_size=5, workers=3, iter=5):
+
+        # pd.read_pickle(self.temp_path+'walks.pkl')
+        sentences = self.sentences
+
+        print("Learning representation...")
+        model = Word2Vec(sentences, size=embed_size, window=window_size, min_count=0, hs=1, sg=1, workers=workers,
+                         iter=iter)
+        print("Learning representation done!")
+        self.w2v_model = model
+
+        return model
+
+    def get_embeddings(self,):
+        if self.w2v_model is None:
+            print("model not train")
+            return {}
+
+        self._embeddings = {}
+        for word in self.graph.nodes():
+            self._embeddings[word] = self.w2v_model.wv[word]
+
+        return self._embeddings
+
+    def _compute_ordered_degreelist(self, max_num_layers):
+
+        degreeList = {}
+        vertices = self.idx  # self.g.nodes()
+        for v in vertices:
+            degreeList[v] = self._get_order_degreelist_node(v, max_num_layers)
+        return degreeList
+
+    def _get_order_degreelist_node(self, root, max_num_layers=None):
+        if max_num_layers is None:
+            max_num_layers = float('inf')
+
+        ordered_degree_sequence_dict = {}
+        visited = [False] * len(self.graph.nodes())
+        queue = deque()
+        level = 0
+        queue.append(root)
+        visited[root] = True
+
+        while (len(queue) > 0 and level <= max_num_layers):
+
+            count = len(queue)
+            if self.opt1_reduce_len:
+                degree_list = {}
+            else:
+                degree_list = []
+            while (count > 0):
+
+                top = queue.popleft()
+                node = self.idx2node[top]
+                degree = len(self.graph[node])
+
+                if self.opt1_reduce_len:
+                    degree_list[degree] = degree_list.get(degree, 0) + 1
+                else:
+                    degree_list.append(degree)
+
+                for nei in self.graph[node]:
+                    nei_idx = self.node2idx[nei]
+                    if not visited[nei_idx]:
+                        visited[nei_idx] = True
+                        queue.append(nei_idx)
+                count -= 1
+            if self.opt1_reduce_len:
+                orderd_degree_list = [(degree, freq)
+                                      for degree, freq in degree_list.items()]
+                orderd_degree_list.sort(key=lambda x: x[0])
+            else:
+                orderd_degree_list = sorted(degree_list)
+            ordered_degree_sequence_dict[level] = orderd_degree_list
+            level += 1
+
+        return ordered_degree_sequence_dict
+
+    def _compute_structural_distance(self, max_num_layers, workers=1, verbose=0,):
+
+        if os.path.exists(self.temp_path+'structural_dist.pkl'):
+            structural_dist = pd.read_pickle(
+                self.temp_path+'structural_dist.pkl')
+        else:
+            if self.opt1_reduce_len:
+                dist_func = cost_max
+            else:
+                dist_func = cost
+
+            if os.path.exists(self.temp_path + 'degreelist.pkl'):
+                degreeList = pd.read_pickle(self.temp_path + 'degreelist.pkl')
+            else:
+                degreeList = self._compute_ordered_degreelist(max_num_layers)
+                pd.to_pickle(degreeList, self.temp_path + 'degreelist.pkl')
+
+            if self.opt2_reduce_sim_calc:
+                degrees = self._create_vectors()
+                degreeListsSelected = {}
+                vertices = {}
+                n_nodes = len(self.idx)
+                for v in self.idx:  # c:list of vertex
+                    nbs = get_vertices(
+                        v, len(self.graph[self.idx2node[v]]), degrees, n_nodes)
+                    vertices[v] = nbs  # store nbs
+                    degreeListsSelected[v] = degreeList[v]  # store dist
+                    for n in nbs:
+                        # store dist of nbs
+                        degreeListsSelected[n] = degreeList[n]
+            else:
+                vertices = {}
+                for v in degreeList:
+                    vertices[v] = [vd for vd in degreeList.keys() if vd > v]
+
+            results = Parallel(n_jobs=workers, verbose=verbose,)(
+                delayed(compute_dtw_dist)(part_list, degreeList, dist_func) for part_list in partition_dict(vertices, workers))
+            dtw_dist = dict(ChainMap(*results))
+
+            structural_dist = convert_dtw_struc_dist(dtw_dist)
+            pd.to_pickle(structural_dist, self.temp_path +
+                         'structural_dist.pkl')
+
+        return structural_dist
+
+    def _create_vectors(self):
+        degrees = {}  # sotre v list of degree
+        degrees_sorted = set()  # store degree
+        G = self.graph
+        for v in self.idx:
+            degree = len(G[self.idx2node[v]])
+            degrees_sorted.add(degree)
+            if (degree not in degrees):
+                degrees[degree] = {}
+                degrees[degree]['vertices'] = []
+            degrees[degree]['vertices'].append(v)
+        degrees_sorted = np.array(list(degrees_sorted), dtype='int')
+        degrees_sorted = np.sort(degrees_sorted)
+
+        l = len(degrees_sorted)
+        for index, degree in enumerate(degrees_sorted):
+            if (index > 0):
+                degrees[degree]['before'] = degrees_sorted[index - 1]
+            if (index < (l - 1)):
+                degrees[degree]['after'] = degrees_sorted[index + 1]
+
+        return degrees
+
+    def _get_layer_rep(self, pair_distances):
+        layer_distances = {}
+        layer_adj = {}
+        for v_pair, layer_dist in pair_distances.items():
+            for layer, distance in layer_dist.items():
+                vx = v_pair[0]
+                vy = v_pair[1]
+
+                layer_distances.setdefault(layer, {})
+                layer_distances[layer][vx, vy] = distance
+
+                layer_adj.setdefault(layer, {})
+                layer_adj[layer].setdefault(vx, [])
+                layer_adj[layer].setdefault(vy, [])
+                layer_adj[layer][vx].append(vy)
+                layer_adj[layer][vy].append(vx)
+
+        return layer_adj, layer_distances
+
+    def _get_transition_probs(self, layers_adj, layers_distances):
+        layers_alias = {}
+        layers_accept = {}
+
+        for layer in layers_adj:
+
+            neighbors = layers_adj[layer]
+            layer_distances = layers_distances[layer]
+            node_alias_dict = {}
+            node_accept_dict = {}
+            norm_weights = {}
+
+            for v, neighbors in neighbors.items():
+                e_list = []
+                sum_w = 0.0
+
+                for n in neighbors:
+                    if (v, n) in layer_distances:
+                        wd = layer_distances[v, n]
+                    else:
+                        wd = layer_distances[n, v]
+                    w = np.exp(-float(wd))
+                    e_list.append(w)
+                    sum_w += w
+
+                e_list = [x / sum_w for x in e_list]
+                norm_weights[v] = e_list
+                accept, alias = create_alias_table(e_list)
+                node_alias_dict[v] = alias
+                node_accept_dict[v] = accept
+
+            pd.to_pickle(
+                norm_weights, self.temp_path + 'norm_weights_distance-layer-' + str(layer)+'.pkl')
+
+            layers_alias[layer] = node_alias_dict
+            layers_accept[layer] = node_accept_dict
+
+        return layers_accept, layers_alias
+
+
+def cost(a, b):
+    ep = 0.5
+    m = max(a, b) + ep
+    mi = min(a, b) + ep
+    return ((m / mi) - 1)
+
+
+def cost_min(a, b):
+    ep = 0.5
+    m = max(a[0], b[0]) + ep
+    mi = min(a[0], b[0]) + ep
+    return ((m / mi) - 1) * min(a[1], b[1])
+
+
+def cost_max(a, b):
+    ep = 0.5
+    m = max(a[0], b[0]) + ep
+    mi = min(a[0], b[0]) + ep
+    return ((m / mi) - 1) * max(a[1], b[1])
+
+
+def convert_dtw_struc_dist(distances, startLayer=1):
+    """
+
+    :param distances: dict of dict
+    :param startLayer:
+    :return:
+    """
+    for vertices, layers in distances.items():
+        keys_layers = sorted(layers.keys())
+        startLayer = min(len(keys_layers), startLayer)
+        for layer in range(0, startLayer):
+            keys_layers.pop(0)
+
+        for layer in keys_layers:
+            layers[layer] += layers[layer - 1]
+    return distances
+
+
+def get_vertices(v, degree_v, degrees, n_nodes):
+    a_vertices_selected = 2 * math.log(n_nodes, 2)
+    vertices = []
+    try:
+        c_v = 0
+
+        for v2 in degrees[degree_v]['vertices']:
+            if (v != v2):
+                vertices.append(v2)  # same degree
+                c_v += 1
+                if (c_v > a_vertices_selected):
+                    raise StopIteration
+
+        if ('before' not in degrees[degree_v]):
+            degree_b = -1
+        else:
+            degree_b = degrees[degree_v]['before']
+        if ('after' not in degrees[degree_v]):
+            degree_a = -1
+        else:
+            degree_a = degrees[degree_v]['after']
+        if (degree_b == -1 and degree_a == -1):
+            raise StopIteration  # not anymore v
+        degree_now = verifyDegrees(degrees, degree_v, degree_a, degree_b)
+        # nearest valid degree
+        while True:
+            for v2 in degrees[degree_now]['vertices']:
+                if (v != v2):
+                    vertices.append(v2)
+                    c_v += 1
+                    if (c_v > a_vertices_selected):
+                        raise StopIteration
+
+            if (degree_now == degree_b):
+                if ('before' not in degrees[degree_b]):
+                    degree_b = -1
+                else:
+                    degree_b = degrees[degree_b]['before']
+            else:
+                if ('after' not in degrees[degree_a]):
+                    degree_a = -1
+                else:
+                    degree_a = degrees[degree_a]['after']
+
+            if (degree_b == -1 and degree_a == -1):
+                raise StopIteration
+
+            degree_now = verifyDegrees(degrees, degree_v, degree_a, degree_b)
+
+    except StopIteration:
+        return list(vertices)
+
+    return list(vertices)
+
+
+def verifyDegrees(degrees, degree_v_root, degree_a, degree_b):
+
+    if(degree_b == -1):
+        degree_now = degree_a
+    elif(degree_a == -1):
+        degree_now = degree_b
+    elif(abs(degree_b - degree_v_root) < abs(degree_a - degree_v_root)):
+        degree_now = degree_b
+    else:
+        degree_now = degree_a
+
+    return degree_now
+
+
+def compute_dtw_dist(part_list, degreeList, dist_func):
+    dtw_dist = {}
+    for v1, nbs in part_list:
+        lists_v1 = degreeList[v1]  # lists_v1 :orderd degree list of v1
+        for v2 in nbs:
+            lists_v2 = degreeList[v2]  # lists_v1 :orderd degree list of v2
+            max_layer = min(len(lists_v1), len(lists_v2))  # valid layer
+            dtw_dist[v1, v2] = {}
+            for layer in range(0, max_layer):
+                dist, path = fastdtw(
+                    lists_v1[layer], lists_v2[layer], radius=1, dist=dist_func)
+                dtw_dist[v1, v2][layer] = dist
+    return dtw_dist
diff --git a/GraphEmbedding-master/examples/struc2vec_flight.py b/GraphEmbedding-master/examples/struc2vec_flight.py
new file mode 100644
index 0000000000000000000000000000000000000000..8863675d4397e9afec6695155d7e2f2161c28512
--- /dev/null
+++ b/GraphEmbedding-master/examples/struc2vec_flight.py
@@ -0,0 +1,88 @@
+import numpy as np
+
+
+
+from ge.classify import read_node_label,Classifier
+
+from ge import Struc2Vec
+
+from sklearn.linear_model import LogisticRegression
+
+
+
+import matplotlib.pyplot as plt
+
+import networkx as nx
+
+from sklearn.manifold import TSNE
+
+
+
+def evaluate_embeddings(embeddings):
+
+    X, Y = read_node_label('../data/flight/labels-brazil-airports.txt',skip_head=True)
+
+    tr_frac = 0.8
+
+    print("Training classifier using {:.2f}% nodes...".format(
+
+        tr_frac * 100))
+
+    clf = Classifier(embeddings=embeddings, clf=LogisticRegression())
+
+    clf.split_train_evaluate(X, Y, tr_frac)
+
+
+
+
+
+def plot_embeddings(embeddings,):
+
+    X, Y = read_node_label('../data/flight/labels-brazil-airports.txt',skip_head=True)
+
+
+
+    emb_list = []
+
+    for k in X:
+
+        emb_list.append(embeddings[k])
+
+    emb_list = np.array(emb_list)
+
+
+
+    model = TSNE(n_components=2)
+
+    node_pos = model.fit_transform(emb_list)
+
+
+
+    color_idx = {}
+
+    for i in range(len(X)):
+
+        color_idx.setdefault(Y[i][0], [])
+
+        color_idx[Y[i][0]].append(i)
+
+
+
+    for c, idx in color_idx.items():
+
+        plt.scatter(node_pos[idx, 0], node_pos[idx, 1], label=c)  # c=node_colors)
+
+    plt.legend()
+
+    plt.show()
+
+if __name__ == "__main__":
+    G = nx.read_edgelist('../data/flight/brazil-airports.edgelist', create_using=nx.DiGraph(), nodetype=None,
+                         data=[('weight', int)])
+
+    model = Struc2Vec(G, 10, 80, workers=4, verbose=40, )
+    model.train()
+    embeddings = model.get_embeddings()
+
+    evaluate_embeddings(embeddings)
+    plot_embeddings(embeddings)
\ No newline at end of file
diff --git a/GraphEmbedding-master/examples/utils.py b/GraphEmbedding-master/examples/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8929eec84e383a900f496baa4cfb804a110c4019
--- /dev/null
+++ b/GraphEmbedding-master/examples/utils.py
@@ -0,0 +1,48 @@
+def preprocess_nxgraph(graph):
+    node2idx = {}
+    idx2node = []
+    node_size = 0
+    for node in graph.nodes():
+        node2idx[node] = node_size
+        idx2node.append(node)
+        node_size += 1
+    return idx2node, node2idx
+
+
+def partition_dict(vertices, workers):
+    batch_size = (len(vertices) - 1) // workers + 1
+    part_list = []
+    part = []
+    count = 0
+    for v1, nbs in vertices.items():
+        part.append((v1, nbs))
+        count += 1
+        if count % batch_size == 0:
+            part_list.append(part)
+            part = []
+    if len(part) > 0:
+        part_list.append(part)
+    return part_list
+
+
+def partition_list(vertices, workers):
+    batch_size = (len(vertices) - 1) // workers + 1
+    part_list = []
+    part = []
+    count = 0
+    for v1, nbs in enumerate(vertices):
+        part.append((v1, nbs))
+        count += 1
+        if count % batch_size == 0:
+            part_list.append(part)
+            part = []
+    if len(part) > 0:
+        part_list.append(part)
+    return part_list
+
+
+def partition_num(num, workers):
+    if num % workers == 0:
+        return [num//workers]*workers
+    else:
+        return [num//workers]*workers + [num % workers]
diff --git a/GraphEmbedding-master/examples/walker.py b/GraphEmbedding-master/examples/walker.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9f6f3a49e2d75f1a6ec17e9af7972bfb5cc9838
--- /dev/null
+++ b/GraphEmbedding-master/examples/walker.py
@@ -0,0 +1,276 @@
+import itertools
+import math
+import random
+
+import numpy as np
+import pandas as pd
+from joblib import Parallel, delayed
+from tqdm import trange
+import alias
+import utils
+
+
+class RandomWalker:
+    def __init__(self, G, p=1, q=1, use_rejection_sampling=0):
+        """
+        :param G:
+        :param p: Return parameter,controls the likelihood of immediately revisiting a node in the walk.
+        :param q: In-out parameter,allows the search to differentiate between “inward” and “outward” nodes
+        :param use_rejection_sampling: Whether to use the rejection sampling strategy in node2vec.
+        """
+        self.G = G
+        self.p = p
+        self.q = q
+        self.use_rejection_sampling = use_rejection_sampling
+        #print("khel hna")
+    def deepwalk_walk(self, walk_length, start_node):
+
+        walk = [start_node]
+        #print("walk",start_node)
+        while len(walk) < walk_length:
+            cur = walk[-1]
+            cur_nbrs = list(self.G.neighbors(cur))
+            if len(cur_nbrs) > 0:
+                walk.append(random.choice(cur_nbrs))
+            else:
+                break
+        return walk
+
+    def node2vec_walk(self, walk_length, start_node):
+
+        G = self.G
+        alias_nodes = self.alias_nodes
+        alias_edges = self.alias_edges
+
+        walk = [start_node]
+
+        while len(walk) < walk_length:
+            cur = walk[-1]
+            cur_nbrs = list(G.neighbors(cur))
+            if len(cur_nbrs) > 0:
+                if len(walk) == 1:
+                    walk.append(
+                        cur_nbrs[alias.alias_sample(alias_nodes[cur][0], alias.alias_nodes[cur][1])])
+                else:
+                    prev = walk[-2]
+                    edge = (prev, cur)
+                    next_node = cur_nbrs[alias.alias_sample(alias_edges[edge][0],
+                                                      alias_edges[edge][1])]
+                    walk.append(next_node)
+            else:
+                break
+
+        return walk
+
+    def node2vec_walk2(self, walk_length, start_node):
+        """
+        Reference:
+        KnightKing: A Fast Distributed Graph Random Walk Engine
+        http://madsys.cs.tsinghua.edu.cn/publications/SOSP19-yang.pdf
+        """
+
+        def rejection_sample(inv_p, inv_q, nbrs_num):
+            upper_bound = max(1.0, max(inv_p, inv_q))
+            lower_bound = min(1.0, min(inv_p, inv_q))
+            shatter = 0
+            second_upper_bound = max(1.0, inv_q)
+            if (inv_p > second_upper_bound):
+                shatter = second_upper_bound / nbrs_num
+                upper_bound = second_upper_bound + shatter
+            return upper_bound, lower_bound, shatter
+
+        G = self.G
+        alias_nodes = self.alias_nodes
+        inv_p = 1.0 / self.p
+        inv_q = 1.0 / self.q
+        walk = [start_node]
+        while len(walk) < walk_length:
+            cur = walk[-1]
+            cur_nbrs = list(G.neighbors(cur))
+            if len(cur_nbrs) > 0:
+                if len(walk) == 1:
+                    walk.append(
+                        cur_nbrs[alias.alias_sample(alias_nodes[cur][0], alias_nodes[cur][1])])
+                else:
+                    upper_bound, lower_bound, shatter = rejection_sample(
+                        inv_p, inv_q, len(cur_nbrs))
+                    prev = walk[-2]
+                    prev_nbrs = set(G.neighbors(prev))
+                    while True:
+                        prob = random.random() * upper_bound
+                        if (prob + shatter >= upper_bound):
+                            next_node = prev
+                            break
+                        next_node = cur_nbrs[alias.alias_sample(
+                            alias_nodes[cur][0], alias_nodes[cur][1])]
+                        if (prob < lower_bound):
+                            break
+                        if (prob < inv_p and next_node == prev):
+                            break
+                        _prob = 1.0 if next_node in prev_nbrs else inv_q
+                        if (prob < _prob):
+                            break
+                    walk.append(next_node)
+            else:
+                break
+        return walk
+
+    def simulate_walks(self, num_walks, walk_length, workers=16, verbose=0):
+
+        G = self.G
+
+        nodes = list(G.nodes())
+
+        results = Parallel(n_jobs=workers, verbose=verbose, )(
+            delayed(self._simulate_walks)(nodes, num, walk_length) for num in
+            utils.partition_num(num_walks, workers))
+
+        walks = list(itertools.chain(*results))
+
+        return walks
+
+    def _simulate_walks(self, nodes, num_walks, walk_length,):
+        walks = []
+        for _ in range(num_walks):
+            print("num_walks",num_walks,len(nodes))
+            random.shuffle(nodes)
+            for v in nodes:
+                #if self.p == 1 and self.q == 1:
+                    walks.append(self.deepwalk_walk(
+                        walk_length=walk_length, start_node=v))
+                #elif self.use_rejection_sampling:
+                #    walks.append(self.node2vec_walk2(
+                #        walk_length=walk_length, start_node=v))
+                #else:
+                #    walks.append(self.node2vec_walk(
+                #        walk_length=walk_length, start_node=v))
+        return walks
+
+    def get_alias_edge(self, t, v):
+        """
+        compute unnormalized transition probability between nodes v and its neighbors give the previous visited node t.
+        :param t:
+        :param v:
+        :return:
+        """
+        G = self.G
+        p = self.p
+        q = self.q
+
+        unnormalized_probs = []
+        for x in G.neighbors(v):
+            weight = G[v][x].get('weight', 1.0)  # w_vx
+            if x == t:  # d_tx == 0
+                unnormalized_probs.append(weight/p)
+            elif G.has_edge(x, t):  # d_tx == 1
+                unnormalized_probs.append(weight)
+            else:  # d_tx > 1
+                unnormalized_probs.append(weight/q)
+        norm_const = sum(unnormalized_probs)
+        normalized_probs = [
+            float(u_prob)/norm_const for u_prob in unnormalized_probs]
+
+        return alias.create_alias_table(normalized_probs)
+
+    def preprocess_transition_probs(self):
+        """
+        Preprocessing of transition probabilities for guiding the random walks.
+        """
+        G = self.G
+        alias_nodes = {}
+        for node in G.nodes():
+            unnormalized_probs = [G[node][nbr].get('weight', 1.0)
+                                  for nbr in G.neighbors(node)]
+            norm_const = sum(unnormalized_probs)
+            normalized_probs = [
+                float(u_prob)/norm_const for u_prob in unnormalized_probs]
+            alias_nodes[node] = alias.create_alias_table(normalized_probs)
+
+        if not self.use_rejection_sampling:
+            alias_edges = {}
+
+            for edge in G.edges():
+                alias_edges[edge] = self.get_alias_edge(edge[0], edge[1])
+                if not G.is_directed():
+                    alias_edges[(edge[1], edge[0])] = self.get_alias_edge(edge[1], edge[0])
+                self.alias_edges = alias_edges
+
+        self.alias_nodes = alias_nodes
+        return
+
+
+class BiasedWalker:
+    def __init__(self, idx2node, temp_path):
+
+        self.idx2node = idx2node
+        self.idx = list(range(len(self.idx2node)))
+        self.temp_path = temp_path
+        pass
+
+    def simulate_walks(self, num_walks, walk_length, stay_prob=0.3, workers=1, verbose=0):
+
+        layers_adj = pd.read_pickle(self.temp_path+'layers_adj.pkl')
+        layers_alias = pd.read_pickle(self.temp_path+'layers_alias.pkl')
+        layers_accept = pd.read_pickle(self.temp_path+'layers_accept.pkl')
+        gamma = pd.read_pickle(self.temp_path+'gamma.pkl')
+        walks = []
+        initialLayer = 0
+
+        nodes = self.idx  # list(self.g.nodes())
+
+        results = Parallel(n_jobs=workers, verbose=verbose, )(
+            delayed(self._simulate_walks)(nodes, num, walk_length, stay_prob, layers_adj, layers_accept, layers_alias, gamma) for num in
+            utils.partition_num(num_walks, workers))
+
+        walks = list(itertools.chain(*results))
+        return walks
+
+    def _simulate_walks(self, nodes, num_walks, walk_length, stay_prob, layers_adj, layers_accept, layers_alias, gamma):
+        walks = []
+        for _ in range(num_walks):
+            random.shuffle(nodes)
+            for v in nodes:
+                walks.append(self._exec_random_walk(layers_adj, layers_accept, layers_alias,
+                                                    v, walk_length, gamma, stay_prob))
+        return walks
+
+    def _exec_random_walk(self, graphs, layers_accept, layers_alias, v, walk_length, gamma, stay_prob=0.3):
+        initialLayer = 0
+        layer = initialLayer
+
+        path = []
+        path.append(self.idx2node[v])
+
+        while len(path) < walk_length:
+            r = random.random()
+            if(r < stay_prob):  # same layer
+                v = chooseNeighbor(v, graphs, layers_alias,
+                                   layers_accept, layer)
+                path.append(self.idx2node[v])
+            else:  # different layer
+                r = random.random()
+                try:
+                    x = math.log(gamma[layer][v] + math.e)
+                    p_moveup = (x / (x + 1))
+                except:
+                    print(layer, v)
+                    raise ValueError()
+
+                if(r > p_moveup):
+                    if(layer > initialLayer):
+                        layer = layer - 1
+                else:
+                    if((layer + 1) in graphs and v in graphs[layer + 1]):
+                        layer = layer + 1
+
+        return path
+
+
+def chooseNeighbor(v, graphs, layers_alias, layers_accept, layer):
+
+    v_list = graphs[layer][v]
+
+    idx = alias.alias_sample(layers_accept[layer][v], layers_alias[layer][v])
+    v = v_list[idx]
+
+    return v
diff --git a/GraphEmbedding-master/ge/models/__init__.py b/GraphEmbedding-master/ge/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2375e9b613aaca5409cf31ff0cb6b970bbbee3e
--- /dev/null
+++ b/GraphEmbedding-master/ge/models/__init__.py
@@ -0,0 +1,8 @@
+from .deepwalk import DeepWalk
+from .node2vec import Node2Vec
+from .line import LINE
+from .sdne import SDNE
+from .struc2vec import Struc2Vec
+
+
+__all__ = ["DeepWalk", "Node2Vec", "LINE", "SDNE", "Struc2Vec"]
diff --git a/GraphEmbedding-master/pics/code.png b/GraphEmbedding-master/pics/code.png
new file mode 100644
index 0000000000000000000000000000000000000000..aa53cbf490a030e4ee2379f31094a1d11259c04f
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diff --git a/GraphEmbedding-master/pics/deepctrbot.png b/GraphEmbedding-master/pics/deepctrbot.png
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index 0000000000000000000000000000000000000000..67d0d23f4fecf925fd260a23425bc3a7acb38a4e
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diff --git a/GraphEmbedding-master/pics/edge_list.png b/GraphEmbedding-master/pics/edge_list.png
new file mode 100644
index 0000000000000000000000000000000000000000..09f69ecb75908bf147149abad5b62037ea8ac704
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diff --git a/GraphEmbedding-master/pics/weichennote.png b/GraphEmbedding-master/pics/weichennote.png
new file mode 100644
index 0000000000000000000000000000000000000000..0b60a2f3fc1c4ee4a0d3a6be0f7c16ee17a1854d
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diff --git a/GraphEmbedding-master/setup.py b/GraphEmbedding-master/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..dca2250c8e1b93098dcc5caa703f24704f08ceac
--- /dev/null
+++ b/GraphEmbedding-master/setup.py
@@ -0,0 +1,55 @@
+import setuptools
+
+
+with open("README.md", "r", encoding="utf8") as fh:
+
+    long_description = fh.read()
+
+
+REQUIRED_PACKAGES = [
+    # 'tensorflow>=1.4.0,<=1.12.0',
+    'gensim==3.6.0',
+    'networkx==2.1',
+    'joblib==0.13.0',
+    'fastdtw==0.3.2',
+    'tqdm',
+    'numpy',
+    'scikit-learn',
+    'pandas',
+    'matplotlib',
+]
+
+
+setuptools.setup(
+
+    name="ge",
+
+    version="0.0.0",
+
+    author="Weichen Shen",
+
+    author_email="wcshen1994@163.com",
+
+    url="https://github.com/shenweichen/GraphEmbedding",
+
+    packages=setuptools.find_packages(exclude=[]),
+
+    python_requires='>=3.4',  # 3.4.6
+
+    install_requires=REQUIRED_PACKAGES,
+
+    extras_require={
+
+        "cpu": ['tensorflow>=1.4.0,!=1.7.*,!=1.8.*'],
+
+        "gpu": ['tensorflow-gpu>=1.4.0,!=1.7.*,!=1.8.*'],
+
+    },
+
+    entry_points={
+
+    },
+    license="MIT license",
+
+
+)
diff --git a/PatternRecognition/.gitignore b/PatternRecognition/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..69fa6ec5807fa2190ce813e32ab044cd137bb7e5
--- /dev/null
+++ b/PatternRecognition/.gitignore
@@ -0,0 +1,14 @@
+*.pyc
+.DS_Store
+target
+bin
+build
+.gradle
+*.iml
+*.ipr
+*.iws
+*.log
+.classpath
+.project
+.settings
+.idea
\ No newline at end of file
diff --git a/PatternRecognition/.vs/ProjectSettings.json b/PatternRecognition/.vs/ProjectSettings.json
new file mode 100644
index 0000000000000000000000000000000000000000..f8b4888565caadc7510be75682268d6c18edd6de
--- /dev/null
+++ b/PatternRecognition/.vs/ProjectSettings.json
@@ -0,0 +1,3 @@
+{
+  "CurrentProjectSetting": null
+}
\ No newline at end of file
diff --git a/PatternRecognition/.vs/VSWorkspaceState.json b/PatternRecognition/.vs/VSWorkspaceState.json
new file mode 100644
index 0000000000000000000000000000000000000000..53e356d73ea5b5421cc58efd95b77a51d8b72e86
--- /dev/null
+++ b/PatternRecognition/.vs/VSWorkspaceState.json
@@ -0,0 +1,8 @@
+{
+  "ExpandedNodes": [
+    "",
+    "\\src"
+  ],
+  "SelectedNode": "\\src\\main.py",
+  "PreviewInSolutionExplorer": false
+}
\ No newline at end of file
diff --git a/PatternRecognition/.vs/node2vec/v16/.suo b/PatternRecognition/.vs/node2vec/v16/.suo
new file mode 100644
index 0000000000000000000000000000000000000000..28d9197933b48a60810e1de8b7f6984eab21ecf5
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diff --git a/PatternRecognition/.vs/slnx.sqlite b/PatternRecognition/.vs/slnx.sqlite
new file mode 100644
index 0000000000000000000000000000000000000000..3a391b9a8743f60fb7add8a286c2aee46a941bdc
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diff --git a/PatternRecognition/LICENSE.md b/PatternRecognition/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..af4a91f64f74568d793db4d74cd42a9ae014408b
--- /dev/null
+++ b/PatternRecognition/LICENSE.md
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2016 Aditya Grover
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/PatternRecognition/README.md b/PatternRecognition/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a6c4e85d9ebf2f559530e9e08ff1fb7c5d15f68d
--- /dev/null
+++ b/PatternRecognition/README.md
@@ -0,0 +1,55 @@
+# node2vec
+
+This repository provides a reference implementation of *node2vec* as described in the paper:<br>
+> node2vec: Scalable Feature Learning for Networks.<br>
+> Aditya Grover and Jure Leskovec.<br>
+> Knowledge Discovery and Data Mining, 2016.<br>
+> <Insert paper link>
+
+The *node2vec* algorithm learns continuous representations for nodes in any (un)directed, (un)weighted graph. Please check the [project page](https://snap.stanford.edu/node2vec/) for more details. 
+
+### Basic Usage
+
+#### Example
+To run *node2vec* on Zachary's karate club network, execute the following command from the project home directory:<br/>
+	``python src/main.py --input graph/karate.edgelist --output emb/karate.emd``
+
+#### Options
+You can check out the other options available to use with *node2vec* using:<br/>
+	``python src/main.py --help``
+
+#### Input
+The supported input format is an edgelist:
+
+	node1_id_int node2_id_int <weight_float, optional>
+		
+The graph is assumed to be undirected and unweighted by default. These options can be changed by setting the appropriate flags.
+
+#### Output
+The output file has *n+1* lines for a graph with *n* vertices. 
+The first line has the following format:
+
+	num_of_nodes dim_of_representation
+
+The next *n* lines are as follows:
+	
+	node_id dim1 dim2 ... dimd
+
+where dim1, ... , dimd is the *d*-dimensional representation learned by *node2vec*.
+
+### Citing
+If you find *node2vec* useful for your research, please consider citing the following paper:
+
+	@inproceedings{node2vec-kdd2016,
+	author = {Grover, Aditya and Leskovec, Jure},
+	 title = {node2vec: Scalable Feature Learning for Networks},
+	 booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
+	 year = {2016}
+	}
+
+
+### Miscellaneous
+
+Please send any questions you might have about the code and/or the algorithm to <adityag@cs.stanford.edu>.
+
+*Note:* This is only a reference implementation of the *node2vec* algorithm and could benefit from several performance enhancement schemes, some of which are discussed in the paper.
diff --git a/PatternRecognition/emb/karate.emb b/PatternRecognition/emb/karate.emb
new file mode 100644
index 0000000000000000000000000000000000000000..68a5648c6804a053e770d90d548473e554526901
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diff --git a/PatternRecognition/node2vec_spark/README.md b/PatternRecognition/node2vec_spark/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..43f6684f3ee409868b581e33895ea521858ee811
--- /dev/null
+++ b/PatternRecognition/node2vec_spark/README.md
@@ -0,0 +1,139 @@
+# node2vec on spark
+
+This library is a implementation using scala for running on spark of *node2vec* as described in the paper:
+> node2vec: Scalable Feature Learning for Networks.
+> Aditya Grover and Jure Leskovec.
+> Knowledge Discovery and Data Mining, 2016.
+> <Insert paper link>
+
+The *node2vec* algorithm learns continuous representations for nodes in any (un)directed, (un)weighted graph. Please check the [project page](https://snap.stanford.edu/node2vec/) for more details. 
+
+
+### Building node2vec_spark
+**In order to build node2vec_spark, use the following:**
+
+```
+$ git clone https://github.com/Skarface-/node2vec.git
+$ mvn clean package
+```
+
+**and requires:**<br/>
+Maven 3.0.5 or newer<br/>
+Java 7+<br/>
+Scala 2.10 or newer.
+
+This will produce jar file in "node2vec_spark/target/"
+
+### Examples
+This library has two functions: *randomwalk* and *embedding*. <br/> 
+These were described in these papers [node2vec: Scalable Feature Learning for Networks](http://arxiv.org/abs/1607.00653) and [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/abs/1301.3781).
+
+### Random walk
+Example:
+	
+	./spark-submit --class com.navercorp.Main \ 
+				   ./node2vec_spark/target/node2vec-0.0.1-SNAPSHOT.jar \
+				   --cmd randomwalk --p 100.0 --q 100.0 --walkLength 40 \
+				   --input <input> --output <output>
+
+#### Options
+Invoke a command without arguments to list available arguments and their default values:
+
+```
+--cmd COMMAND
+	Functions: randomwalk or embedding. If you want to execute all functions "randomwalk" and "embedding" sequentially input "node2vec". Default "node2vec"
+--input [INPUT]
+	Input edgelist path. The supported input format is an edgelist: "node1_id_int node2_id_int <weight_float, optional>"
+--output [OUTPUT]
+	Random paths path.
+--walkLength WALK_LENGTH
+	Length of walk per source. Default is 80.
+--numWalks NUM_WALKS
+	Number of walks per source. Default is 10.
+--p P
+	Return hyperparaemter. Default is 1.0.
+--q Q
+	Inout hyperparameter. Default is 1.0.
+--weighted Boolean
+	Specifying (un)weighted. Default is true.
+--directed Boolean
+	Specifying (un)directed. Default is false.
+--degree UPPER_BOUND_OF_NUMBER_OF_NEIGHBORS
+	Specifying upper bound of number of neighbors. Default is 30.
+--indexed Boolean
+	Specifying whether nodes in edgelist are indexed or not. Default is true.
+```
+
+* If "indexed" is set to false, *node2vec_spark* index nodes in input edgelist, example: <br/>
+  **unindexed edgelist:**<br/>
+  node1 	node2 	1.0<br/>
+  node2 	node7 	1.0<br/>
+  
+  **indexed:**<br/>
+  1 	2 	1.0<br/>
+  2 	3 	1.0<br/>
+
+  1 	node1<br/>
+  2 	node2<br/>
+  3 	node7
+
+#### Input
+The supported input format is an edgelist:
+
+	node1_id_int 	node2_id_int 	<weight_float, optional>
+	or
+	node1_str 	node2_str 	<weight_float, optional>, Please set the option "indexed" to false
+
+
+#### Output
+The output file (number of nodes)*numWalks random paths as follows:
+
+	src_node_id_int 	node1_id_int 	node2_id_int 	... 	noden_id_int
+
+
+### Embedding random paths
+Example:
+	
+	./spark-submit --class com.navercorp.Main \
+				   ./node2vec_spark/target/node2vec-0.0.1-SNAPSHOT.jar \
+				   --cmd embedding --dim 50 --iter 20 \
+				   --input <input> --nodePath <node2id_path> --output <output>
+
+#### Options
+Invoke a command without arguments to list available arguments and their default values:
+
+```
+--cmd COMMAND
+	embedding. If you want to execute sequentially all functions: "randomwalk" and "embedding", input "node2vec". default "node2vec"
+--input [INPUT]
+	Input random paths. The supported input format is an random paths: "src_node_id_int node1_id_int ... noden_id_int"
+--output [OUTPUT]
+	word2vec model(.bin) and embeddings(.emb).
+--nodePath [NODE\_PATH]
+	Input node2index path. The supported input format: "node1_str node1_id_int"
+--iter ITERATION
+	Number of epochs in SGD. Default 10.
+--dim DIMENSION
+	Number of dimensions. Default is 128.
+--window WINDOW_SIZE
+	Context size for optimization. Default is 10.
+
+```
+
+#### Input
+The supported input format is an random paths:
+
+	src_node_id_int 	node1_id_int 	... 	noden_id_int
+
+#### Output
+The output files are **embeddings and word2vec model.** The embeddings file has the following format: 
+
+	node1_str 	dim1 dim2 ... dimd
+
+where dim1, ... , dimd is the d-dimensional representation learned by word2vec.
+
+the output file *word2vec model* has the spark word2vec model format. please reference to https://spark.apache.org/docs/1.5.2/mllib-feature-extraction.html#word2vec
+
+## References
+1. [node2vec: Scalable Feature Learning for Networks](http://arxiv.org/abs/1607.00653)
+2. [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/abs/1301.3781)
\ No newline at end of file
diff --git a/PatternRecognition/node2vec_spark/pom.xml b/PatternRecognition/node2vec_spark/pom.xml
new file mode 100644
index 0000000000000000000000000000000000000000..b958576139df8d08ceca4e7fa2c148fe54d8de05
--- /dev/null
+++ b/PatternRecognition/node2vec_spark/pom.xml
@@ -0,0 +1,129 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
+
+    <modelVersion>4.0.0</modelVersion>
+    
+    <groupId>com.navercorp</groupId>
+    <artifactId>node2vec</artifactId>
+    <packaging>jar</packaging>
+    <version>0.0.1-SNAPSHOT</version>
+    
+    <name>node2vec_spark</name>
+    <url>http://snap.stanford.edu/node2vec/</url>
+
+    <properties>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <shadedClassifier>bin</shadedClassifier>
+        <maven-shade-plugin.version>2.4.3</maven-shade-plugin.version>
+        <exec-maven-plugin.version>1.4.0</exec-maven-plugin.version>
+        <java.version>1.7</java.version>
+        <scala.binary.version>2.10</scala.binary.version>
+    </properties>
+
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.scala-tools</groupId>
+                <artifactId>maven-scala-plugin</artifactId>
+                <version>2.15.2</version>
+                <executions>
+                    <execution>
+                        <goals>
+                            <goal>compile</goal>
+                        </goals>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-dependency-plugin</artifactId>
+                <version>2.4</version>
+                <executions>
+                    <execution>
+                        <id>copy-dependencies</id>
+                        <phase>package</phase>
+                        <goals>
+                            <goal>copy-dependencies</goal>
+                        </goals>
+                        <configuration>
+                            <outputDirectory>${project.build.directory}/lib</outputDirectory>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-shade-plugin</artifactId>
+                <version>1.6</version>
+                <executions>
+                    <execution>
+                        <phase>package</phase>
+                        <goals>
+                            <goal>shade</goal>
+                        </goals>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <version>2.3.2</version>
+                <configuration>
+                    <source>1.7</source>
+                    <target>1.7</target>
+                    <encoding>UTF-8</encoding>
+                </configuration>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-surefire-plugin</artifactId>
+                <configuration>
+                    <skip>false</skip>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
+    
+    <dependencies>
+        <dependency>
+            <groupId>org.apache.hadoop</groupId>
+            <artifactId>hadoop-hdfs</artifactId>
+            <version>2.7.1</version>
+        </dependency>
+        <dependency>
+            <groupId>org.scala-lang</groupId>
+            <artifactId>scala-library</artifactId>
+            <version>${scala.binary.version}.5</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-core_${scala.binary.version}</artifactId>
+            <version>1.6.1</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-mllib_${scala.binary.version}</artifactId>
+            <version>1.6.1</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>com.github.scopt</groupId>
+            <artifactId>scopt_${scala.binary.version}</artifactId>
+            <version>3.3.0</version>
+            <exclusions>
+                <exclusion>
+                    <groupId>org.scala-lang</groupId>
+                    <artifactId>scala-library</artifactId>
+                </exclusion>
+            </exclusions>
+        </dependency>
+        <dependency>
+            <groupId>com.google.guava</groupId>
+            <artifactId>guava</artifactId>
+            <version>19.0</version>
+        </dependency>
+    </dependencies>
+
+</project>
diff --git a/PatternRecognition/node2vec_spark/src/main/resources/log4j2.properties b/PatternRecognition/node2vec_spark/src/main/resources/log4j2.properties
new file mode 100644
index 0000000000000000000000000000000000000000..d941e1b93b743323b4d3dacdfbb14b7a39af33bb
--- /dev/null
+++ b/PatternRecognition/node2vec_spark/src/main/resources/log4j2.properties
@@ -0,0 +1,9 @@
+
+appender.out.type = Console
+appender.out.name = out
+appender.out.layout.type = PatternLayout
+appender.out.layout.pattern = [%30.30t] %-30.30c{1} %-5p %m%n
+logger.springframework.name = org.springframework
+logger.springframework.level = WARN
+rootLogger.level = INFO
+rootLogger.appenderRef.out.ref = out
diff --git a/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Main.scala b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Main.scala
new file mode 100644
index 0000000000000000000000000000000000000000..f3494e54ee87f5d7b51ee2e8d56b143ca6a4359a
--- /dev/null
+++ b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Main.scala
@@ -0,0 +1,119 @@
+package com.navercorp
+
+import java.io.Serializable
+import org.apache.spark.{SparkContext, SparkConf}
+import scopt.OptionParser
+import com.navercorp.lib.AbstractParams
+
+object Main {
+  object Command extends Enumeration {
+    type Command = Value
+    val node2vec, randomwalk, embedding = Value
+  }
+  import Command._
+
+  case class Params(iter: Int = 10,
+                    lr: Double = 0.025,
+                    numPartition: Int = 10,
+                    dim: Int = 128,
+                    window: Int = 10,
+                    walkLength: Int = 80,
+                    numWalks: Int = 10,
+                    p: Double = 1.0,
+                    q: Double = 1.0,
+                    weighted: Boolean = true,
+                    directed: Boolean = false,
+                    degree: Int = 30,
+                    indexed: Boolean = true,
+                    nodePath: String = null,
+                    input: String = null,
+                    output: String = null,
+                    cmd: Command = Command.node2vec) extends AbstractParams[Params] with Serializable
+  val defaultParams = Params()
+  
+  val parser = new OptionParser[Params]("Node2Vec_Spark") {
+    head("Main")
+    opt[Int]("walkLength")
+            .text(s"walkLength: ${defaultParams.walkLength}")
+            .action((x, c) => c.copy(walkLength = x))
+    opt[Int]("numWalks")
+            .text(s"numWalks: ${defaultParams.numWalks}")
+            .action((x, c) => c.copy(numWalks = x))
+    opt[Double]("p")
+            .text(s"return parameter p: ${defaultParams.p}")
+            .action((x, c) => c.copy(p = x))
+    opt[Double]("q")
+            .text(s"in-out parameter q: ${defaultParams.q}")
+            .action((x, c) => c.copy(q = x))
+    opt[Boolean]("weighted")
+            .text(s"weighted: ${defaultParams.weighted}")
+            .action((x, c) => c.copy(weighted = x))
+    opt[Boolean]("directed")
+            .text(s"directed: ${defaultParams.directed}")
+            .action((x, c) => c.copy(directed = x))
+    opt[Int]("degree")
+            .text(s"degree: ${defaultParams.degree}")
+            .action((x, c) => c.copy(degree = x))
+    opt[Boolean]("indexed")
+            .text(s"Whether nodes are indexed or not: ${defaultParams.indexed}")
+            .action((x, c) => c.copy(indexed = x))
+    opt[String]("nodePath")
+            .text("Input node2index file path: empty")
+            .action((x, c) => c.copy(nodePath = x))
+    opt[String]("input")
+            .required()
+            .text("Input edge file path: empty")
+            .action((x, c) => c.copy(input = x))
+    opt[String]("output")
+            .required()
+            .text("Output path: empty")
+            .action((x, c) => c.copy(output = x))
+    opt[String]("cmd")
+            .required()
+            .text(s"command: ${defaultParams.cmd.toString}")
+            .action((x, c) => c.copy(cmd = Command.withName(x)))
+    note(
+      """
+        |For example, the following command runs this app on a synthetic dataset:
+        |
+        | bin/spark-submit --class com.nhn.sunny.vegapunk.ml.model.Node2vec \
+      """.stripMargin +
+              s"|   --lr ${defaultParams.lr}" +
+              s"|   --iter ${defaultParams.iter}" +
+              s"|   --numPartition ${defaultParams.numPartition}" +
+              s"|   --dim ${defaultParams.dim}" +
+              s"|   --window ${defaultParams.window}" +
+              s"|   --input <path>" +
+              s"|   --node <nodeFilePath>" +
+              s"|   --output <path>"
+    )
+  }
+  
+  def main(args: Array[String]) = {
+    parser.parse(args, defaultParams).map { param =>
+      val conf = new SparkConf().setAppName("Node2Vec")
+      val context: SparkContext = new SparkContext(conf)
+      
+      Node2vec.setup(context, param)
+      
+      param.cmd match {
+        case Command.node2vec => Node2vec.load()
+                                         .initTransitionProb()
+                                         .randomWalk()
+                                         .embedding()
+                                         .save()
+        case Command.randomwalk => Node2vec.load()
+                                           .initTransitionProb()
+                                           .randomWalk()
+                                           .saveRandomPath()
+        case Command.embedding => {
+          val randomPaths = Word2vec.setup(context, param).read(param.input)
+          Word2vec.fit(randomPaths).save(param.output)
+          Node2vec.loadNode2Id(param.nodePath).saveVectors()
+        }
+      }
+    } getOrElse {
+      sys.exit(1)
+    }
+  }
+}
diff --git a/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Node2vec.scala b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Node2vec.scala
new file mode 100644
index 0000000000000000000000000000000000000000..07ec21a1d2cd04b9ae920804c42ad1a9d6049cea
--- /dev/null
+++ b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Node2vec.scala
@@ -0,0 +1,281 @@
+package com.navercorp
+
+
+import java.io.Serializable
+import scala.util.Try
+import scala.collection.mutable.ArrayBuffer
+import org.slf4j.{Logger, LoggerFactory}
+import org.apache.spark.SparkContext
+import org.apache.spark.rdd.RDD
+import org.apache.spark.graphx.{EdgeTriplet, Graph, _}
+import com.navercorp.graph.{GraphOps, EdgeAttr, NodeAttr}
+
+object Node2vec extends Serializable {
+  lazy val logger: Logger = LoggerFactory.getLogger(getClass.getName);
+  
+  var context: SparkContext = null
+  var config: Main.Params = null
+  var node2id: RDD[(String, Long)] = null
+  var indexedEdges: RDD[Edge[EdgeAttr]] = _
+  var indexedNodes: RDD[(VertexId, NodeAttr)] = _
+  var graph: Graph[NodeAttr, EdgeAttr] = _
+  var randomWalkPaths: RDD[(Long, ArrayBuffer[Long])] = null
+
+  def setup(context: SparkContext, param: Main.Params): this.type = {
+    this.context = context
+    this.config = param
+    
+    this
+  }
+  
+  def load(): this.type = {
+    val bcMaxDegree = context.broadcast(config.degree)
+    val bcEdgeCreator = config.directed match {
+      case true => context.broadcast(GraphOps.createDirectedEdge)
+      case false => context.broadcast(GraphOps.createUndirectedEdge)
+    }
+    
+    val inputTriplets: RDD[(Long, Long, Double)] = config.indexed match {
+      case true => readIndexedGraph(config.input)
+      case false => indexingGraph(config.input)
+    }
+    
+    indexedNodes = inputTriplets.flatMap { case (srcId, dstId, weight) =>
+      bcEdgeCreator.value.apply(srcId, dstId, weight)
+    }.reduceByKey(_++_).map { case (nodeId, neighbors: Array[(VertexId, Double)]) =>
+      var neighbors_ = neighbors
+      if (neighbors_.length > bcMaxDegree.value) {
+        neighbors_ = neighbors.sortWith{ case (left, right) => left._2 > right._2 }.slice(0, bcMaxDegree.value)
+      }
+        
+      (nodeId, NodeAttr(neighbors = neighbors_.distinct))
+    }.repartition(200).cache
+    
+    indexedEdges = indexedNodes.flatMap { case (srcId, clickNode) =>
+      clickNode.neighbors.map { case (dstId, weight) =>
+          Edge(srcId, dstId, EdgeAttr())
+      }
+    }.repartition(200).cache
+    
+    this
+  }
+  
+  def initTransitionProb(): this.type = {
+    val bcP = context.broadcast(config.p)
+    val bcQ = context.broadcast(config.q)
+    
+    graph = Graph(indexedNodes, indexedEdges)
+            .mapVertices[NodeAttr] { case (vertexId, clickNode) =>
+              val (j, q) = GraphOps.setupAlias(clickNode.neighbors)
+              val nextNodeIndex = GraphOps.drawAlias(j, q)
+              clickNode.path = Array(vertexId, clickNode.neighbors(nextNodeIndex)._1)
+              
+              clickNode
+            }
+            .mapTriplets { edgeTriplet: EdgeTriplet[NodeAttr, EdgeAttr] =>
+              val (j, q) = GraphOps.setupEdgeAlias(bcP.value, bcQ.value)(edgeTriplet.srcId, edgeTriplet.srcAttr.neighbors, edgeTriplet.dstAttr.neighbors)
+              edgeTriplet.attr.J = j
+              edgeTriplet.attr.q = q
+              edgeTriplet.attr.dstNeighbors = edgeTriplet.dstAttr.neighbors.map(_._1)
+              
+              edgeTriplet.attr
+            }.cache
+    
+    this
+  }
+  
+  def randomWalk(): this.type = {
+    val edge2attr = graph.triplets.map { edgeTriplet =>
+      (s"${edgeTriplet.srcId}${edgeTriplet.dstId}", edgeTriplet.attr)
+    }.repartition(200).cache
+    edge2attr.first
+    
+    for (iter <- 0 until config.numWalks) {
+      var prevWalk: RDD[(Long, ArrayBuffer[Long])] = null
+      var randomWalk = graph.vertices.map { case (nodeId, clickNode) =>
+        val pathBuffer = new ArrayBuffer[Long]()
+        pathBuffer.append(clickNode.path:_*)
+        (nodeId, pathBuffer) 
+      }.cache
+      var activeWalks = randomWalk.first
+      graph.unpersist(blocking = false)
+      graph.edges.unpersist(blocking = false)
+      for (walkCount <- 0 until config.walkLength) {
+        prevWalk = randomWalk
+        randomWalk = randomWalk.map { case (srcNodeId, pathBuffer) =>
+          val prevNodeId = pathBuffer(pathBuffer.length - 2)
+          val currentNodeId = pathBuffer.last
+
+          (s"$prevNodeId$currentNodeId", (srcNodeId, pathBuffer))
+        }.join(edge2attr).map { case (edge, ((srcNodeId, pathBuffer), attr)) =>
+          try {
+            val nextNodeIndex = GraphOps.drawAlias(attr.J, attr.q)
+            val nextNodeId = attr.dstNeighbors(nextNodeIndex)
+            pathBuffer.append(nextNodeId)
+
+            (srcNodeId, pathBuffer)
+          } catch {
+            case e: Exception => throw new RuntimeException(e.getMessage)
+          }
+        }.cache
+
+        activeWalks = randomWalk.first()
+        prevWalk.unpersist(blocking=false)
+      }
+
+
+      if (randomWalkPaths != null) {
+        val prevRandomWalkPaths = randomWalkPaths
+        randomWalkPaths = randomWalkPaths.union(randomWalk).cache()
+        randomWalkPaths.first
+        prevRandomWalkPaths.unpersist(blocking = false)
+      } else {
+        randomWalkPaths = randomWalk
+      }
+    }
+    
+    this
+  }
+  
+  def embedding(): this.type = {
+    val randomPaths = randomWalkPaths.map { case (vertexId, pathBuffer) =>
+      Try(pathBuffer.map(_.toString).toIterable).getOrElse(null)
+    }.filter(_!=null)
+    
+    Word2vec.setup(context, config).fit(randomPaths)
+    
+    this
+  }
+  
+  def save(): this.type = {
+    this.saveRandomPath()
+        .saveModel()
+        .saveVectors()
+  }
+  
+  def saveRandomPath(): this.type = {
+    randomWalkPaths
+            .map { case (vertexId, pathBuffer) =>
+              Try(pathBuffer.mkString("\t")).getOrElse(null)
+            }
+            .filter(x => x != null && x.replaceAll("\\s", "").length > 0)
+            .repartition(200)
+            .saveAsTextFile(config.output)
+    
+    this
+  }
+  
+  def saveModel(): this.type = {
+    Word2vec.save(config.output)
+    
+    this
+  }
+  
+  def saveVectors(): this.type = {
+    val node2vector = context.parallelize(Word2vec.getVectors.toList)
+            .map { case (nodeId, vector) =>
+              (nodeId.toLong, vector.mkString(","))
+            }
+    
+    if (this.node2id != null) {
+      val id2Node = this.node2id.map{ case (strNode, index) =>
+        (index, strNode)
+      }
+      
+      node2vector.join(id2Node)
+              .map { case (nodeId, (vector, name)) => s"$name\t$vector" }
+              .repartition(200)
+              .saveAsTextFile(s"${config.output}.emb")
+    } else {
+      node2vector.map { case (nodeId, vector) => s"$nodeId\t$vector" }
+              .repartition(200)
+              .saveAsTextFile(s"${config.output}.emb")
+    }
+    
+    this
+  }
+  
+  def cleanup(): this.type = {
+    node2id.unpersist(blocking = false)
+    indexedEdges.unpersist(blocking = false)
+    indexedNodes.unpersist(blocking = false)
+    graph.unpersist(blocking = false)
+    randomWalkPaths.unpersist(blocking = false)
+    
+    this
+  }
+
+  def loadNode2Id(node2idPath: String): this.type = {
+    try {
+      this.node2id = context.textFile(config.nodePath).map { node2index =>
+        val Array(strNode, index) = node2index.split("\\s")
+        (strNode, index.toLong)
+      }
+    } catch {
+      case e: Exception => logger.info("Failed to read node2index file.")
+      this.node2id = null
+    }
+
+    this
+  }
+  
+  def readIndexedGraph(tripletPath: String) = {
+    val bcWeighted = context.broadcast(config.weighted)
+    
+    val rawTriplets = context.textFile(tripletPath)
+    if (config.nodePath == null) {
+      this.node2id = createNode2Id(rawTriplets.map { triplet =>
+        val parts = triplet.split("\\s")
+        (parts.head, parts(1), -1)
+      })
+    } else {
+      loadNode2Id(config.nodePath)
+    }
+
+    rawTriplets.map { triplet =>
+      val parts = triplet.split("\\s")
+      val weight = bcWeighted.value match {
+        case true => Try(parts.last.toDouble).getOrElse(1.0)
+        case false => 1.0
+      }
+
+      (parts.head.toLong, parts(1).toLong, weight)
+    }
+  }
+  
+
+  def indexingGraph(rawTripletPath: String): RDD[(Long, Long, Double)] = {
+    val rawEdges = context.textFile(rawTripletPath).map { triplet =>
+      val parts = triplet.split("\\s")
+
+      Try {
+        (parts.head, parts(1), Try(parts.last.toDouble).getOrElse(1.0))
+      }.getOrElse(null)
+    }.filter(_!=null)
+    
+    this.node2id = createNode2Id(rawEdges)
+    
+    rawEdges.map { case (src, dst, weight) =>
+      (src, (dst, weight))
+    }.join(node2id).map { case (src, (edge: (String, Double), srcIndex: Long)) =>
+      try {
+        val (dst: String, weight: Double) = edge
+        (dst, (srcIndex, weight))
+      } catch {
+        case e: Exception => null
+      }
+    }.filter(_!=null).join(node2id).map { case (dst, (edge: (Long, Double), dstIndex: Long)) =>
+      try {
+        val (srcIndex, weight) = edge
+        (srcIndex, dstIndex, weight)
+      } catch {
+        case e: Exception => null
+      }
+    }.filter(_!=null)
+  }
+  
+  def createNode2Id[T <: Any](triplets: RDD[(String, String, T)]) = triplets.flatMap { case (src, dst, weight) =>
+    Try(Array(src, dst)).getOrElse(Array.empty[String])
+  }.distinct().zipWithIndex()
+
+}
diff --git a/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Word2vec.scala b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Word2vec.scala
new file mode 100644
index 0000000000000000000000000000000000000000..aa209cf6534f795d1a0aed932ec322841fe193c6
--- /dev/null
+++ b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Word2vec.scala
@@ -0,0 +1,55 @@
+package com.navercorp
+
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.feature.{Word2Vec, Word2VecModel}
+import org.apache.spark.rdd.RDD
+
+object Word2vec extends Serializable {
+  var context: SparkContext = null
+  var word2vec = new Word2Vec()
+  var model: Word2VecModel = null
+  
+  def setup(context: SparkContext, param: Main.Params): this.type = {
+    this.context = context
+    /**
+      * model = sg
+      * update = hs
+      */
+    word2vec.setLearningRate(param.lr)
+            .setNumIterations(param.iter)
+            .setNumPartitions(param.numPartition)
+            .setMinCount(0)
+            .setVectorSize(param.dim)
+
+    val word2vecWindowField = word2vec.getClass.getDeclaredField("org$apache$spark$mllib$feature$Word2Vec$$window")
+    word2vecWindowField.setAccessible(true)
+    word2vecWindowField.setInt(word2vec, param.window)
+    
+    this
+  }
+  
+  def read(path: String): RDD[Iterable[String]] = {
+    context.textFile(path).repartition(200).map(_.split("\\s").toSeq)
+  }
+  
+  def fit(input: RDD[Iterable[String]]): this.type = {
+    model = word2vec.fit(input)
+    
+    this
+  }
+  
+  def save(outputPath: String): this.type = {
+    model.save(context, s"$outputPath.bin")
+    this
+  }  
+  
+  def load(path: String): this.type = {
+    model = Word2VecModel.load(context, path)
+    
+    this
+  }
+  
+  def getVectors = this.model.getVectors
+  
+}
+
diff --git a/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/graph/GraphOps.scala b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/graph/GraphOps.scala
new file mode 100644
index 0000000000000000000000000000000000000000..960fa8c9dbcd8437770b422666fe38d0cef75529
--- /dev/null
+++ b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/graph/GraphOps.scala
@@ -0,0 +1,69 @@
+package com.navercorp.graph
+
+import scala.collection.mutable.ArrayBuffer
+
+object GraphOps {
+  def setupAlias(nodeWeights: Array[(Long, Double)]): (Array[Int], Array[Double]) = {
+    val K = nodeWeights.length
+    val J = Array.fill(K)(0)
+    val q = Array.fill(K)(0.0)
+
+    val smaller = new ArrayBuffer[Int]()
+    val larger = new ArrayBuffer[Int]()
+
+    val sum = nodeWeights.map(_._2).sum
+    nodeWeights.zipWithIndex.foreach { case ((nodeId, weight), i) =>
+      q(i) = K * weight / sum
+      if (q(i) < 1.0) {
+        smaller.append(i)
+      } else {
+        larger.append(i)
+      }
+    }
+
+    while (smaller.nonEmpty && larger.nonEmpty) {
+      val small = smaller.remove(smaller.length - 1)
+      val large = larger.remove(larger.length - 1)
+
+      J(small) = large
+      q(large) = q(large) + q(small) - 1.0
+      if (q(large) < 1.0) smaller.append(large)
+      else larger.append(large)
+    }
+
+    (J, q)
+  }
+
+  def setupEdgeAlias(p: Double = 1.0, q: Double = 1.0)(srcId: Long, srcNeighbors: Array[(Long, Double)], dstNeighbors: Array[(Long, Double)]): (Array[Int], Array[Double]) = {
+    val neighbors_ = dstNeighbors.map { case (dstNeighborId, weight) =>
+      var unnormProb = weight / q
+      if (srcId == dstNeighborId) unnormProb = weight / p
+      else if (srcNeighbors.exists(_._1 == dstNeighborId)) unnormProb = weight
+
+      (dstNeighborId, unnormProb)
+    }
+
+    setupAlias(neighbors_)
+  }
+
+  def drawAlias(J: Array[Int], q: Array[Double]): Int = {
+    val K = J.length
+    val kk = math.floor(math.random * K).toInt
+
+    if (math.random < q(kk)) kk
+    else J(kk)
+  }
+
+  lazy val createUndirectedEdge = (srcId: Long, dstId: Long, weight: Double) => {
+    Array(
+      (srcId, Array((dstId, weight))),
+      (dstId, Array((srcId, weight)))
+    )
+  }
+  
+  lazy val createDirectedEdge = (srcId: Long, dstId: Long, weight: Double) => {
+    Array(
+      (srcId, Array((dstId, weight)))
+    )
+  }
+}
diff --git a/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/graph/package.scala b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/graph/package.scala
new file mode 100644
index 0000000000000000000000000000000000000000..1b83969c62887ef7f54f1dfc7a1279ab1f4a6476
--- /dev/null
+++ b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/graph/package.scala
@@ -0,0 +1,12 @@
+package com.navercorp
+
+import java.io.Serializable
+
+package object graph {
+  case class NodeAttr(var neighbors: Array[(Long, Double)] = Array.empty[(Long, Double)],
+                      var path: Array[Long] = Array.empty[Long]) extends Serializable
+
+  case class EdgeAttr(var dstNeighbors: Array[Long] = Array.empty[Long],
+                      var J: Array[Int] = Array.empty[Int],
+                      var q: Array[Double] = Array.empty[Double]) extends Serializable
+}
diff --git a/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/lib/AbstractParams.scala b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/lib/AbstractParams.scala
new file mode 100644
index 0000000000000000000000000000000000000000..0790ab9cefa18b4809fe62c29a33f1da30dd56d1
--- /dev/null
+++ b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/lib/AbstractParams.scala
@@ -0,0 +1,53 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.navercorp.lib
+
+import scala.reflect.runtime.universe._
+
+/**
+  * Abstract class for parameter case classes.
+  * This overrides the [[toString]] method to print all case class fields by name and value.
+  * @tparam T  Concrete parameter class.
+  */
+abstract class AbstractParams[T: TypeTag] {
+
+  private def tag: TypeTag[T] = typeTag[T]
+
+  /**
+    * Finds all case class fields in concrete class instance, and outputs them in JSON-style format:
+    * {
+    *   [field name]:\t[field value]\n
+    *   [field name]:\t[field value]\n
+    *   ...
+    * }
+    */
+  override def toString: String = {
+    val tpe = tag.tpe
+    val allAccessors = tpe.declarations.collect {
+      case m: MethodSymbol if m.isCaseAccessor => m
+    }
+    val mirror = runtimeMirror(getClass.getClassLoader)
+    val instanceMirror = mirror.reflect(this)
+    allAccessors.map { f =>
+      val paramName = f.name.toString
+      val fieldMirror = instanceMirror.reflectField(f)
+      val paramValue = fieldMirror.get
+      s"  $paramName:\t$paramValue"
+    }.mkString("{\n", ",\n", "\n}")
+  }
+}
\ No newline at end of file
diff --git a/PatternRecognition/requirements.txt b/PatternRecognition/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..faa710ca36b26473e27bb1f74af311dd0b0b156d
--- /dev/null
+++ b/PatternRecognition/requirements.txt
@@ -0,0 +1,3 @@
+networkx==1.11
+numpy==1.11.2
+gensim==0.13.3
diff --git a/PatternRecognition/src/Bipartie_convert.py b/PatternRecognition/src/Bipartie_convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..017bfe1048d2ec82ec1f0355190c1c0a36023058
--- /dev/null
+++ b/PatternRecognition/src/Bipartie_convert.py
@@ -0,0 +1,150 @@
+import networkx as nx
+import matplotlib.pyplot as plt
+import numpy as np
+import time
+from multiprocessing import Pool
+from itertools import repeat
+from ANN_CLIQUES import PatternFinding
+from train import Training
+
+def localisation(G,i):
+    X = []
+    a = list(G.neighbors(i))
+    if len(G.edges(i)) > 10:
+            for j in sorted(G.nodes()):
+                """
+                if j > i and G.number_of_edges(i, j) == 0:
+                    X.append(j)
+                else:
+                    Y.append(j)
+                """
+                if j > i and G.number_of_edges(i,j) == 0 and len(G.edges(j)) > 10:
+                    V = []
+                    V.append(i)
+                    V.append(j)
+
+                    b = list(G.neighbors(j))
+                    c = list(set(a).intersection(b))
+                    t = sorted(c)
+                    if len(c) > 0 and t[0] > V[0]:
+                        V = V + c
+                    """
+
+                    for k in c:
+                        if i != j and G.number_of_edges(i, j) == 0 and G.number_of_edges(i,k) == 1 and G.number_of_edges(j, k) == 1:
+                            V.append(k)
+                        else:
+                            if i != j and G.number_of_edges(i, j) == 1 and G.number_of_edges(i,k) == 0 and k in G.nodes() and G.number_of_edges(j, k) == 0 and Voisin(k, V, G):
+                                V.append(k)
+
+                    #print("le commun entre : ",i,j,list(set(a) & set(b)))
+                    for k in G.nodes():
+                        if i != j and G.number_of_edges(i,j) == 0 and G.number_of_edges(i,k) == 1 and G.number_of_edges(j,k) == 1:
+                            V.append(k)
+                        else:
+                            if i != j and G.number_of_edges(i,j) == 1 and G.number_of_edges(i,k) == 0 and k in G.nodes() and G.number_of_edges(j,k) == 0 and Voisin(k,V,G):
+                                V.append(k)
+                    """
+                    if len(V) > 3:
+                        X.append(V)
+            return X
+    else:
+            return None
+
+def Voisin(c,G):
+    k = []
+    for i in range(len(c)):
+            for j in range(len(c)):
+                if j > i and G.number_of_edges(c[j],c[i]) == 0:
+                    return False
+    return True
+if __name__ == "__main__":
+    M = []
+    fh = open("C:/Users/LENOVO/Desktop/karate.edgelist", "rb")
+    G = nx.read_edgelist(fh, nodetype=int)
+    node_nums = G.number_of_nodes()
+    y = node_nums % 100
+    if y != 0:
+        for i in range(100 - y):
+            G.add_node(node_nums + i)
+    node_num = G.number_of_nodes()
+
+    bipartie = []
+
+    T = nx.edges(G)
+    T = np.asarray(T)
+    O = nx.Graph()
+    O.add_edges_from(T, nodetype=int)
+    start = time.time()
+    print("====== Bipartite Start =======")
+    with Pool() as p:
+        x = p.starmap(localisation, zip(repeat(O), sorted(O.nodes())))
+        bipartie.append(x)
+    end = time.time()
+    print("======= End of first step =======")
+    print("Time", end - start)
+
+    flat_list = [item for sublist in bipartie for item in sublist if item]
+    #print("flat 1",len(flat_list)) #,flat_list)
+    flat_list = [item for sublist in flat_list for item in sublist]
+    #print("flat 2",len(flat_list))#,flat_list)
+    np.save('graph/test_Bip.npy', flat_list)
+
+    new_k = []
+    k_new = []
+    for elem in flat_list:
+        x = True
+        if sorted(elem) not in new_k:
+            for elem2 in new_k:
+                if set(elem).issubset(set(elem2)) and elem != elem2:
+                    x = False
+            if x:
+                new_k.append(sorted(elem))
+                k_new.append(elem)
+    bipartie = k_new
+
+    sz = []
+    flat_list = [item for sublist in flat_list for item in sublist]
+    R = np.unique(flat_list)
+
+    #print("R",np.unique(R),len(np.unique(R)))
+    node_bip = np.zeros(300)
+    node_bip[np.unique(R)] = 1
+    #print(node_bip)
+    PatternFinding(G.edges, node_bip)
+    Training()
+    fo = open("example1.model", "a")
+    stri = "bc "
+    for i in flat_list:
+        x = True
+        strt = ' '.join(map(str, i[0:2]))
+        stry = ' '.join(map(str, i[-(len(i) - 2):]))
+        fo.write(stri + strt + ',' + stry + "\n")
+    fo.close()
+    fh = open("C:/Users/LENOVO/Desktop/karate.edgelist", "rb")
+    G = nx.read_edgelist(fh)
+    T = nx.edges(G)
+    T = np.asarray(T)
+    E = T
+    data = []
+    for a, b in G.edges():
+        data.append([a, b])
+    data = np.asarray(data)
+    data = np.expand_dims(data, axis=0)
+
+    for i in range(len(E)):
+        x = E[i, 0]
+        c = E[i, 1]
+        if (int(x) not in R or int(c) not in R):
+            G.remove_edge(x,c)
+    T = nx.edges(G)
+    T = np.asarray(T)
+    end = time.time()
+    print("======= End of seconde step =======")
+    print("Time", end - start)
+    sz.append(T)
+    np.save('graph/test.npy', data)
+    np.save('graph/sam.npy', sz[0])
+    np.save('graph/labfin.npy', node_bip)
+
+
diff --git a/PatternRecognition/src/Clique-Adapt.py b/PatternRecognition/src/Clique-Adapt.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c7fc0f32d493b351f563e6816182afa21da755c
--- /dev/null
+++ b/PatternRecognition/src/Clique-Adapt.py
@@ -0,0 +1,14 @@
+import itertools
+
+L = [[ 269 ,  240 ,  232 ], [ 269 ,  240 ,  293 ], [ 269 ,  268 ,  178 ], [ 269 ,  30 ], [ 269 ,  56 ,  154 ], [ 269 ,  154 ,  232 ], [ 269 ,  293 ,  258 ,  246 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  22 ,  238 ,  239 ,  202 ,  232 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  22 ,  238 ,  239 ,  202 ,  247 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  238 ,  202 ,  232 ,  239 ,  219 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  238 ,  202 ,  232 ,  239 ,  250 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  238 ,  202 ,  232 ,  241 ,  219 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  238 ,  202 ,  232 ,  241 ,  250 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  238 ,  202 ,  258 ,  247 ,  239 ,  219 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  238 ,  202 ,  258 ,  247 ,  239 ,  250 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  238 ,  202 ,  258 ,  247 ,  241 ,  219 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  238 ,  202 ,  258 ,  247 ,  241 ,  250 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  238 ,  246 ,  239 ,  232 ,  219 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  238 ,  246 ,  239 ,  232 ,  250 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  238 ,  246 ,  239 ,  247 ,  258 ,  219 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  238 ,  246 ,  239 ,  247 ,  258 ,  250 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  164 ,  258 ,  247 ,  250 ,  239 ,  246 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  171 ,  249 ,  164 ,  258 ,  247 ,  250 ,  241 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  62 ,  246 ,  249 ,  232 ,  239 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  62 ,  246 ,  249 ,  232 ,  217 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  62 ,  246 ,  249 ,  247 ,  239 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  62 ,  246 ,  249 ,  247 ,  217 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  261 ,  219 ,  238 ,  249 ,  232 ,  202 ,  179 ,  239 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  261 ,  219 ,  238 ,  249 ,  232 ,  202 ,  179 ,  241 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  261 ,  219 ,  238 ,  249 ,  232 ,  202 ,  217 ,  241 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  261 ,  219 ,  238 ,  249 ,  232 ,  241 ,  205 ,  217 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  261 ,  219 ,  238 ,  249 ,  232 ,  246 ,  179 ,  239 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  261 ,  219 ,  238 ,  249 ,  232 ,  246 ,  205 ,  239 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  261 ,  219 ,  238 ,  249 ,  232 ,  246 ,  205 ,  217 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  261 ,  219 ,  238 ,  249 ,  258 ,  179 ,  177 ,  241 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  261 ,  219 ,  238 ,  249 ,  258 ,  179 ,  177 ,  246 ], [ 269 ,  282 ,  283 ,  278 ,  279 ,  262 ,  261 ,  219 ,  238 ,  249 ,  258 ,  179 ,  202 ,  239 ], [ 269 ,  282 ,  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75 ,  298 ,  295 ,  259 ,  274 ,  297 ,  294 ,  284 ,  271 ], [ 75 ,  298 ,  295 ,  259 ,  296 ,  289 ,  227 ,  213 ], [ 75 ,  298 ,  295 ,  259 ,  296 ,  289 ,  227 ,  271 ], [ 75 ,  298 ,  295 ,  259 ,  296 ,  289 ,  284 ,  213 ,  280 ], [ 75 ,  298 ,  295 ,  259 ,  296 ,  289 ,  284 ,  271 ], [ 75 ,  298 ,  295 ,  259 ,  296 ,  294 ,  227 ,  213 ], [ 75 ,  298 ,  295 ,  259 ,  296 ,  294 ,  227 ,  271 ], [ 75 ,  298 ,  295 ,  259 ,  296 ,  294 ,  284 ,  213 ,  280 ], [ 75 ,  298 ,  295 ,  259 ,  296 ,  294 ,  284 ,  271 ], [ 75 ,  298 ,  295 ,  267 ,  274 ,  297 ,  289 ,  271 ,  227 ], [ 75 ,  298 ,  295 ,  267 ,  274 ,  297 ,  289 ,  271 ,  284 ], [ 75 ,  298 ,  295 ,  267 ,  274 ,  297 ,  289 ,  280 ,  284 ], [ 75 ,  298 ,  295 ,  267 ,  274 ,  297 ,  294 ,  271 ,  227 ], [ 75 ,  298 ,  295 ,  267 ,  274 ,  297 ,  294 ,  271 ,  284 ], [ 75 ,  298 ,  295 ,  267 ,  274 ,  297 ,  294 ,  280 ,  284 ], [ 75 ,  298 ,  295 ,  267 ,  296 ,  289 ,  271 ,  227 ], [ 75 ,  298 ,  295 ,  267 ,  296 ,  289 ,  271 ,  284 ], [ 75 ,  298 ,  295 ,  267 ,  296 ,  289 ,  280 ,  284 ], [ 75 ,  298 ,  295 ,  267 ,  296 ,  294 ,  271 ,  227 ], [ 75 ,  298 ,  295 ,  267 ,  296 ,  294 ,  271 ,  284 ], [ 75 ,  298 ,  295 ,  267 ,  296 ,  294 ,  280 ,  284 ], [ 116 ,  254 ,  29 ], [ 116 ,  254 ,  264 ], [ 116 ,  29 ,  172 ], [ 241 ,  11 ,  270 ], [ 241 ,  170 ], [ 219 ,  52 ], [ 292 ,  227 ,  83 ,  271 ], [ 292 ,  227 ,  286 ,  271 ], [ 292 ,  227 ,  286 ,  293 ], [ 275 ,  71 ], [ 275 ,  135 ,  257 ], [ 134 ,  240 ,  289 ,  274 ], [ 134 ,  240 ,  289 ,  296 ], [ 257 ,  267 ,  170 ], [ 186 ,  264 ,  270 ], [ 276 ,  228 ,  296 ], [ 71 ,  230 ], [ 90 ,  293 ,  242 ]]
+K = L
+for i in L:
+    for j in L:
+        if i[0] == j[0]:
+            for k in j:
+                if k not in i:
+                    i.append(k)
+                    i.sort()
+L.sort()
+K = list(L for L,_ in itertools.groupby(L))
+print(K)
\ No newline at end of file
diff --git a/PatternRecognition/src/Compact_Clique.py b/PatternRecognition/src/Compact_Clique.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce1cf42c6cb743a36a2e2534eefaec09c9244c40
--- /dev/null
+++ b/PatternRecognition/src/Compact_Clique.py
@@ -0,0 +1,41 @@
+import numpy as np
+
+
+def Filewrite2(E,fo):
+    for i in E:
+        if len(i) > 5:
+            stri = "nc "
+            print("i ",i)
+            stri = stri + str(i[0]) + ", "
+            for j in i:
+                if j != i[0]:
+                    stri = stri + str(j) + " "
+            strt = stri[-3:]
+            if ',' not in strt:
+                fo.write(stri + "\n")
+
+#L = np.load("graph/test_Star.npy", allow_pickle=True)
+C = np.load("graph/test_Cliques.npy", allow_pickle=True)
+
+#print(L)
+print(C)
+T = []
+k = []
+for i in C:
+    if i not in k:
+        x = i
+        m = i
+        print("hay x",x)
+        for j in C:
+            if j != i and j[0] == i[0]:
+                x = x+j
+                [m.append(obj) for obj in x if obj not in m]
+                k.append(j)
+        print("X",m)
+        T.append(m)
+print(C)
+print(T,len(T))
+print(k)
+fo = open("example1.model", "a")
+Filewrite2(T,fo)
+fo.close()
diff --git a/PatternRecognition/src/Edge.py b/PatternRecognition/src/Edge.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b2b081698ece653fddcf3cac37c4311905b5e64
--- /dev/null
+++ b/PatternRecognition/src/Edge.py
@@ -0,0 +1,73 @@
+
+import networkx as nx
+import matplotlib.pyplot as plt
+import numpy as np
+import time
+
+def Recuperation(G):
+    #recuperation de tous les noeuds du graph avec une taille > x.
+    T = []
+    for i in G.nodes():
+        if len(G.edges(i)) >= 2:
+            T.append(i)
+    Suppression(T,G)
+def Suppression(T,G):
+    #retirer les liens avec les noeuds non recupérer
+    E = nx.edges(G)
+    E = np.asarray(E)
+    M = []
+    for i in range(len(E)):
+        if E[i,0] not in T or E[i,1] not in T:
+            M.append(i)
+
+    size = len(E)
+    E = [v for i,v in enumerate(E) if i not in M]
+    E = np.array(E)
+    new_size = len(E)
+    if size != new_size:
+        O = nx.Graph()
+        O.add_edges_from(E, nodetype=int)
+        Recuperation(O)
+    else:
+        O = nx.Graph()
+        O.add_edges_from(E, nodetype=int)
+
+def Filewrite(E):
+    stri = "fc "
+    fo = open("example1.model", "a")
+    R = []
+    for i in E:
+        x = True
+        for j in E:
+            if set(i).issubset(set(j)) and i != j:
+                x = False
+        if x:
+            R.append(set(i))
+            strt = ' '.join(map(str, i))
+            fo.write(stri + strt + "\n")
+    fo.close()
+    print("The file contains: ")
+    fo = open("example1.model", "r")
+    dummy = fo.read()
+    print(dummy)
+    fo.close()
+    import os
+    print(os.path.abspath("example1.model"))
+
+fh = open("C:/Users/LENOVO/Desktop/karate.edgelist", "rb")
+G = nx.read_edgelist(fh,nodetype=int)
+x = G.number_of_nodes()
+print(nx.number_of_nodes(G))
+y = x % 100
+if y != 0:
+    for i in range(100-y):
+        G.add_node(x+i)
+start = time.time()
+x = list(nx.find_cliques(G))
+Filewrite(x)
+print(time.time()-start)
+start = time.time()
+Recuperation(G)
+Filewrite(x)
+print(time.time()-start)
+
diff --git a/PatternRecognition/src/EdgeConert_Star.py b/PatternRecognition/src/EdgeConert_Star.py
new file mode 100644
index 0000000000000000000000000000000000000000..130418b6c4920d9e2226456eb3657f5845a7f551
--- /dev/null
+++ b/PatternRecognition/src/EdgeConert_Star.py
@@ -0,0 +1,175 @@
+import networkx as nx
+import numpy as np
+import time
+from ANN_CLIQUES import PatternFinding
+from train import Training
+from compact import Compact
+import matplotlib.pyplot as plt
+fh = open("C:/Users/LENOVO/Desktop/karate.edgelist", "rb")
+G = nx.read_edgelist(fh,nodetype=int)
+#nx.draw(G, node_size = 900)
+#plt.show()
+W = G.number_of_nodes()
+x = list(set(list(G.nodes)))
+size = x[len(x)-1]
+print("====== Start stars ======")
+y = (size+1) % 100
+if y != 0:
+    for i in range(100-y):
+        G.add_node(W+i)
+        #print("n",x+i)
+total_size = G.number_of_nodes()
+#print(total_size)
+outU = np.zeros(size + 1 + 100 -y)
+U = list(sorted(list(G.nodes)))
+start = time.time()
+for i in U:
+    if len(list(G.neighbors(i))) > 1:
+        outU[i] = 1
+        outU[list(G.neighbors(i))] = 1
+#print(outU)
+PatternFinding(G.edges,outU)
+Training()
+#print(type(G.edges))
+#print(G.nodes())
+
+#print(U)
+#print("haylik",U[1])
+#A = nx.adjacency_matrix(G, nodelist=sorted(G.nodes()), weight='weight')
+#A.setdiag(A.diagonal() * 2)
+#A = A.todense()
+#print(A[0])
+#B = copy.deepcopy(A)
+#print("ici",list(G.neighbors(91)),len(list(G.neighbors(91))),len(list(G.neighbors(1))))
+#fo = open("example1.model", "w")
+R = []
+for i in range(W):
+    #stri = "st "
+    s = []
+    x = True
+    v = list(G.neighbors(U[i]))
+    if (len(v) < 1):
+        #B[i] = 0
+        continue
+    else:
+        #print(U[i])
+        #print(U[i])
+        s.append(U[i])
+        #fo.write(stri + str(i+1) + ", ")
+        #stri = stri + str(U[i]) + ", "
+        #print("voisinnnnnns",U[i],list(G.neighbors(U[i])))
+        m = sorted(list(G.neighbors(U[i])))
+        #m = list(U[i]) + m
+        s = s + m
+        #stri = stri + str(m) + " "
+        #for j in m:
+        #    if j not in output:
+        #        output.append(j)
+            #stri = stri + str(j) + " "
+
+        """
+        for j in range(W):
+            #print("ici",len(list(G.neighbors(j+1))))
+            if G.number_of_edges(U[i], U[j]) >= 1:
+                #sortie[j] = 1
+                #fo.write(str(j+1) + " ")
+                s.append(U[j])
+                stri = stri + str(U[j]) + " "
+                if U[j] not in output:
+                    output.append(U[j])
+        """
+        #strt = stri[-3:]
+        if len(s) > 2:#',' not in strt:
+            #for j in R:
+            #    if set(s).issubset(set(j)):
+            #        print("babaha", set(s), set(j))
+            #        x = False
+            #if x:
+                R.append(s)
+            #    print("sss",stri)
+                #fo.write(stri + "\n")
+            #else:
+            #    print("letoile", U[i])
+np.save('graph/test_Star.npy', R)
+#fo.close()
+Compact(R)
+print("====== End of step succesful ======")
+print("Time",time.time()-start)
+#print(len(output),output)
+#nx.draw(G,cmap=plt.get_cmap('viridis'),with_labels=True)
+#plt.show()
+""" ###################################################
+
+M = []
+data=[]
+x = time.time()
+print("debut bendada")
+for a,b in G.edges():
+    data.append([a,b])
+y = time.time()
+print("fin bendada",y-x,len(list(G.nodes)))
+data = np.asarray(data)
+data = np.expand_dims(data,axis=0)
+np.save('graph/test_Bip.npy',data)
+
+print(data)
+
+Z = nx.Graph()
+
+for i in range(len(sortie)):
+    #print("i",i)
+    for j in range(len(sortie)):
+        if sortie[i] == 1 and sortie[j] == 1 and i != j:
+            Z.add_edge(str(i),str(j))
+#print("Z",Z.edges)
+sz = []
+T = nx.edges(Z)
+T = np.asarray(T)
+#print(M,len(M))
+#print(output)
+########################################
+fh = open("C:/Users/LENOVO/Desktop/karate.edgelist", "rb")
+
+print("hay len ya t7a7na", len(T))
+E = T
+for i in range(len(E)):
+    #print("ca rentre")
+    x = E[i, 0]
+    c = E[i, 1]
+    #print("x",len(np.argwhere(T == x)))
+    #print("c",len(np.argwhere(T == c)))
+
+    #print("hnnnnnnnaaaaaaaayyyyyyaaaaaaaa",x,np.argwhere(T == x),len(np.argwhere(T == x)))
+    if (len(np.argwhere(T == x)) < 2) or (len(np.argwhere(T == c)) < 2):
+        #print("c'est ce lien qui va etre supp " , c , x)
+        w = -1
+        t = np.argwhere(T == (x, c))
+        d = np.argwhere(T == (c, x))
+        t = np.concatenate((t, d))
+        for r in range(len(t)):
+            for k in range(len(t)):
+                if (t[r, 0] == t[k, 0]) and r != k and w != t[r, 0]:
+                    w = t[r, 0]
+                    break
+        P = np.delete(T, w, axis=0)
+        T = P
+#################################################
+print("====== End of second step ======")
+end = time.time()
+print("Time",end-start)
+
+sz.append(T)
+#print("uuuuuuuuuuuuuuuu uuuuuuuuuuuuuu",sz[0])
+#print(np.count_nonzero(A[0] == 1))
+#print(np.count_nonzero(sortie == 1))
+#print(np.count_nonzero(B == 1))
+np.save('graph/test.npy', data)
+np.save('graph/sam.npy', sz[0])
+#print(sz[0],len(sz[0]))
+#O = nx.Graph()
+#O.add_edges_from(sz[0],nodetype=int)
+#nx.draw(O,cmap=plt.get_cmap('viridis'),with_labels=True)
+#plt.show()
+np.save('graph/labfin.npy',sortie)
+
+"""
\ No newline at end of file
diff --git a/PatternRecognition/src/EdgeConvert.py b/PatternRecognition/src/EdgeConvert.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c970bfb06c3bac89dc504ec54cdb2356b10b489
--- /dev/null
+++ b/PatternRecognition/src/EdgeConvert.py
@@ -0,0 +1,100 @@
+import networkx as nx
+import matplotlib.pyplot as plt
+import numpy as np
+import copy
+from networkx.algorithms import clique
+import time
+from ANN_CLIQUES import PatternFinding
+from train import Training
+
+def Filewrite(E):
+    stri = "fc "
+    fo = open("example1.model", "a")
+    R = []
+    for i in E:
+        #x = True
+        #for j in E:
+        #    if set(i).issubset(set(j)) and i != j:
+        #        x = False
+        #if x:
+            #R.append(set(i))
+            strt = ' '.join(map(str, i))
+            fo.write(stri + strt + "\n")
+    fo.close()
+    import os
+    #print(os.path.abspath("example1.model"))
+#print("lancement")
+fh = open("C:/Users/LENOVO/Desktop/karate.edgelist", "rb")
+start = time.time()
+G = nx.read_edgelist(fh)
+x = G.number_of_nodes()
+#print(x)
+print("====== Start Cliques ======")
+y = x % 100
+if y != 0:
+    for i in range(100-y):
+        G.add_node(x+i)
+
+total_size = G.number_of_nodes()
+cliques = list(clique.find_cliques(G))
+flat_list = [sublist for sublist in cliques if len(sublist) > 5]
+cliques = np.asarray(([y for x in flat_list for y in x if len(x) >= 5]))
+
+#print("cliques",flat_list,len(flat_list))
+fo = open("example1.model", "w")
+#fo.write(str(flat_list))
+#fo.close()
+
+node_cliques = np.unique(cliques)
+nodes_cliques = node_cliques.astype(int)
+
+val_map = {}
+key = []
+i = 0
+values = [val_map.get(node, 0.25) for node in G.nodes()]
+
+""" pour les quasi-cliques
+key=np.zeros(G.number_of_nodes())
+for clique in cliques:
+    for node in clique:
+        val_map[node]=1.0
+        key[int(node)] = 1
+        i = i+1
+key[nodes_cliques]=1
+"""
+output = np.zeros(G.number_of_nodes())
+output[nodes_cliques] = 1
+#print(output)
+end = time.time()
+print("====== End of first step ======")
+print("Time",end-start)
+M = []
+data=[]
+for a,b in G.edges():
+    data.append([a,b])
+data = np.asarray(data)
+data = np.expand_dims(data,axis=0)
+PatternFinding(G.edges,output)
+Training()
+
+Filewrite(flat_list)
+np.save('graph/test_Cliques.npy', flat_list)
+sz = []
+
+T = nx.edges(G)
+E = np.asarray(T)
+for i in range(len(E)):
+    x = E[i, 0]
+    c = E[i, 1]
+    if (x not in node_cliques) or (c not in node_cliques):
+            G.remove_edge(x, c)
+    T = nx.edges(G)
+    T = np.asarray(T)
+sz.append(T)
+#print(sz)
+end = time.time()
+print("====== End of second step ======")
+print("Time",end-start)
+np.save('graph/test.npy', data)
+np.save('graph/sam.npy', sz[0])
+np.save('graph/labfin.npy',output)
diff --git a/PatternRecognition/src/File_addaptation.py b/PatternRecognition/src/File_addaptation.py
new file mode 100644
index 0000000000000000000000000000000000000000..e66fd892fd6f75055e325c5ca2820118f9dcbb95
--- /dev/null
+++ b/PatternRecognition/src/File_addaptation.py
@@ -0,0 +1,136 @@
+
+f = open("C:/Users/LENOVO/Desktop/karate.edgelist","r")
+paragraph = f.readlines()
+f1 = open("C:/Users/LENOVO/Desktop/o.txt","w")
+print("hna")
+r = []
+
+for line in paragraph:
+    #f1.write(line.strip()+",1\n")
+    tmp = line.strip().split(',')
+    print(tmp[0])
+    print(tmp[1])
+    #x = []
+    #x.append(int(tmp[0]))
+    #x.append(int(tmp[1]))
+    #if tmp[0] != tmp[1] and sorted(x) not in r:
+    #r.append(sorted(x))
+    f1.write(str(int(tmp[0]))+","+str(int(tmp[1]))+"\n")
+
+    #f1.write("x["+str(int(tmp[0]))+","+str(int(tmp[1]))+"] <- NA\n")
+"""
+import networkx as nx
+import matplotlib.pyplot as plt
+
+G = nx.Graph()
+G.add_edge(11,17,color='r')
+G.add_edge(11,12,color='r')
+G.add_edge(11,7,color='r')
+G.add_edge(12,17,color='r')
+G.add_edge(12,7,color='r')
+G.add_edge(12,5,color='r')
+G.add_edge(12,6,color='r')
+G.add_edge(5,6,color='r')
+G.add_edge(5,17,color='r')
+G.add_edge(1,17,color='r')
+G.add_edge(13,2,color='r')
+G.add_edge(10,2,color='r')
+G.add_edge(32,24,color='r')
+G.add_edge(32,28,color='r')
+G.add_edge(24,29,color='r')
+G.add_edge(25,24,color='r')
+G.add_edge(28,26,color='r')
+G.add_edge(28,29,color='r')
+G.add_edge(29,26,color='r')
+G.add_edge(29,25,color='r')
+
+
+G.add_edge(5,11,color='g')
+G.add_edge(5,7,color='g')
+G.add_edge(5,1,color='g')
+G.add_edge(6,17,color='g')
+G.add_edge(6,7,color='g')
+G.add_edge(6,11,color='g')
+G.add_edge(1,6,color='g')
+G.add_edge(1,7,color='g')
+G.add_edge(7,17,color='g')
+G.add_edge(1,11,color='g')
+G.add_edge(1,12,color='g')
+G.add_edge(1,12,color='g')
+G.add_edge(2,22,color='g')
+G.add_edge(20,2,color='g')
+G.add_edge(2,4,color='g')
+G.add_edge(2,8,color='g')
+G.add_edge(2,3,color='g')
+G.add_edge(2,18,color='g')
+G.add_edge(2,14,color='g')
+G.add_edge(9,34,color='g')
+G.add_edge(15,34,color='g')
+G.add_edge(16,34,color='g')
+G.add_edge(19,34,color='g')
+G.add_edge(21,34,color='g')
+G.add_edge(23,34,color='g')
+G.add_edge(24,28,color='g')
+G.add_edge(24,26,color='g')
+G.add_edge(25,32,color='g')
+G.add_edge(25,28,color='g')
+G.add_edge(25,26,color='g')
+G.add_edge(26,32,color='g')
+G.add_edge(27,34,color='g')
+G.add_edge(29,32,color='g')
+G.add_edge(30,34,color='g')
+G.add_edge(31,34,color='g')
+G.add_edge(33,34,color='g')
+
+G.add_edge(1,32,color='black')
+G.add_edge(1,22,color='black')
+G.add_edge(1,20,color='black')
+G.add_edge(1,18,color='black')
+G.add_edge(1,14,color='black')
+G.add_edge(1,13,color='black')
+G.add_edge(1,9,color='black')
+G.add_edge(1,8,color='black')
+G.add_edge(1,4,color='black')
+G.add_edge(1,3,color='black')
+G.add_edge(1,2,color='black')
+G.add_edge(2,31,color='black')
+G.add_edge(3,15,color='black')
+G.add_edge(3,9,color='black')
+G.add_edge(3,10,color='black')
+G.add_edge(3,33,color='black')
+G.add_edge(3,29,color='black')
+G.add_edge(3,28,color='black')
+G.add_edge(3,8,color='black')
+G.add_edge(3,4,color='black')
+G.add_edge(4,14,color='black')
+G.add_edge(4,13,color='black')
+G.add_edge(4,8,color='black')
+G.add_edge(9,33,color='black')
+G.add_edge(10,34,color='black')
+G.add_edge(14,34,color='black')
+G.add_edge(15,33,color='black')
+G.add_edge(16,33,color='black')
+G.add_edge(19,33,color='black')
+G.add_edge(20,34,color='black')
+G.add_edge(21,33,color='black')
+G.add_edge(23,33,color='black')
+G.add_edge(24,30,color='black')
+G.add_edge(24,34,color='black')
+G.add_edge(24,33,color='black')
+G.add_edge(27,30,color='black')
+G.add_edge(28,34,color='black')
+G.add_edge(29,34,color='black')
+G.add_edge(30,33,color='black')
+G.add_edge(31,33,color='black')
+G.add_edge(32,34,color='black')
+G.add_edge(32,33,color='black')
+
+colors = nx.get_edge_attributes(G,'color').values()
+
+pos = nx.circular_layout(G)
+nx.draw(G,
+        edge_color=colors,
+        with_labels=True,node_size = 800)
+
+#nx.draw(G, with_labels='True',node_size = 1000, edge_color = ['red','red','red','red','red','red','red','red','red','red','red','red','red','red','red','red','red','red','red','red','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black'])
+plt.show()"""
\ No newline at end of file
diff --git a/PatternRecognition/src/Final_model.h5 b/PatternRecognition/src/Final_model.h5
new file mode 100644
index 0000000000000000000000000000000000000000..f12e7d7206da529af1ef8bc4b4630674f2e629d6
Binary files /dev/null and b/PatternRecognition/src/Final_model.h5 differ
diff --git a/PatternRecognition/src/INoutput_data_val.npy b/PatternRecognition/src/INoutput_data_val.npy
new file mode 100644
index 0000000000000000000000000000000000000000..88379bba7a83d0501c5f2b464643063ad9c85f16
Binary files /dev/null and b/PatternRecognition/src/INoutput_data_val.npy differ
diff --git a/PatternRecognition/src/compact.py b/PatternRecognition/src/compact.py
new file mode 100644
index 0000000000000000000000000000000000000000..46447c7fec41a2a1d553e154e2bf9b6ca5606125
--- /dev/null
+++ b/PatternRecognition/src/compact.py
@@ -0,0 +1,41 @@
+from multiprocessing import Pool
+from itertools import repeat
+import numpy as np
+import copy
+from compact2 import Compact2
+def Filewrite(E):
+    fo = open("example1.model", "w")
+    for i in E:
+        stri = "st "
+        print("i ",i)
+        stri = stri + str(i[0]) + ", "
+        for j in i:
+            if j != i[0]:
+                stri = stri + str(j) + " "
+        strt = stri[-3:]
+        if ',' not in strt:
+            fo.write(stri + "\n")
+    fo.close()
+def localisation(L,i):
+    for j in L:
+            if j != i and i[0] in j:
+                i.remove(j[0])
+    return i
+def Compact(flat_list):
+    print("=======Start compact========")
+
+    new_L = []
+    L2 = copy.deepcopy(flat_list)
+    for i in range(len(flat_list)):
+        if len(flat_list[i]) > 2:
+            for j in range(len(flat_list)):
+                if len(flat_list[j]) > 2:
+                    x = flat_list[i]
+                    if flat_list[i] != flat_list[j] and x[0] in L2[j]:
+                        y = flat_list[j]
+                        if y[0] in L2[i]:
+                            L2[i].remove(y[0])
+            new_L.append(L2[i])
+    np.save('graph/test_Star.npy', new_L)
+    print("wsel")
+    Compact2(new_L)
\ No newline at end of file
diff --git a/PatternRecognition/src/compact2.py b/PatternRecognition/src/compact2.py
new file mode 100644
index 0000000000000000000000000000000000000000..2210fa83c045ae7e4d673452a3ff219c86addef5
--- /dev/null
+++ b/PatternRecognition/src/compact2.py
@@ -0,0 +1,50 @@
+
+def Filewrite(E):
+    fo = open("example1.model", "w")
+    #f = open("patterns.txt","w")
+    for i in E:
+        print(type(i))
+        #f.write(i)
+        print(i)
+        if len(i) > 1:
+            stri = "st "
+            stri = stri + str(i[0]) + ", "
+            for j in i:
+                if j != i[0]:
+                    stri = stri + str(j) + " "
+            strt = stri[-3:]
+            if ',' not in strt:
+                fo.write(stri + "\n")
+    fo.close()
+def Compact2(L):
+
+    for i in range(len(L)):
+        x = L[-i - 1]
+        for j in range(len(L)):
+            y = L[j]
+            if y[0] in x and x[0] > y[0]:
+                L[-i - 1].remove(y[0])
+                if x[0] not in L[j]:
+                    L[j].append(x[0])
+
+    L = [sublist for sublist in L if sublist != []]
+
+    for i in range(len(L)):
+        t = 0
+        v = []
+        x = L[i]
+        for j in range(len(L)):
+            r = L[j]
+            if i != j and len(r) > 0 and r[0] in x:
+                t = t + 1
+                v.append(j)
+
+        if t == len(x) - 1:
+            for k in v:
+                if x[0] not in L[k]:
+                    L[k].append(x[0])
+            L[i] = []
+
+    L = [sublist for sublist in L if sublist != []]
+    Filewrite(L)
+
diff --git a/PatternRecognition/src/compacte3 b/PatternRecognition/src/compacte3
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/PatternRecognition/src/compacte3.py b/PatternRecognition/src/compacte3.py
new file mode 100644
index 0000000000000000000000000000000000000000..f417c6a6068c6ba048a4481126b1e24282b49dd9
--- /dev/null
+++ b/PatternRecognition/src/compacte3.py
@@ -0,0 +1,52 @@
+import numpy as np
+
+def Filewrite(E,fo):
+    for i in E:
+        if len(i) > 3:
+            stri = "st "
+            print("i ",i)
+            stri = stri + str(i[0]) + ", "
+            for j in i:
+                if j != i[0]:
+                    stri = stri + str(j) + " "
+            strt = stri[-3:]
+            if ',' not in strt:
+                fo.write(stri + "\n")
+
+def Filewrite2(E,fo):
+    stri = "fc "
+    R = []
+    for i in E:
+        x = True
+        for j in E:
+            if set(i).issubset(set(j)) and i != j or len(i) < 4:
+                x = False
+        if x:
+            R.append(set(i))
+            strt = ' '.join(map(str, i))
+            fo.write(stri + strt + "\n")
+
+L = np.load("graph/test_Star.npy", allow_pickle=True)
+C = np.load("graph/test_Cliques.npy", allow_pickle=True)
+
+print(L)
+T = []
+for i in L:
+    size = len(i)
+    for j in C:
+        supp = []
+        if len(i) > 0 and str(i[0]) in j:
+            for z in range(len(i)):
+                if i[z] != i[0] and str(i[z]) in j:
+                    supp.append(i[z])
+            if len(supp) > 0 and j not in T:
+                T.append(j)
+                for k in supp:
+                    i.remove(k)
+print(L)
+print(T,len(T),len(C))
+fo = open("example1.model", "w")
+Filewrite(L,fo)
+Filewrite2(T,fo)
+fo.close()
+np.save('graph/test_Cliques.npy', T)
diff --git a/PatternRecognition/src/example1.model b/PatternRecognition/src/example1.model
new file mode 100644
index 0000000000000000000000000000000000000000..8614af85f655ab16bdb52f04a85be9c44c12c213
--- /dev/null
+++ b/PatternRecognition/src/example1.model
@@ -0,0 +1,448 @@
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diff --git a/PatternRecognition/src/graph/Graphes.npy b/PatternRecognition/src/graph/Graphes.npy
new file mode 100644
index 0000000000000000000000000000000000000000..2d6141ef8fa1ba88227c74deb27157caa1a065ed
Binary files /dev/null and b/PatternRecognition/src/graph/Graphes.npy differ
diff --git a/PatternRecognition/src/graph/data.npy b/PatternRecognition/src/graph/data.npy
new file mode 100644
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diff --git a/PatternRecognition/src/graph/test_Star.npy b/PatternRecognition/src/graph/test_Star.npy
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diff --git a/PatternRecognition/src/main.py b/PatternRecognition/src/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..1405ac8928a9e5d93d7429f571cfe4cac07c1f82
--- /dev/null
+++ b/PatternRecognition/src/main.py
@@ -0,0 +1,160 @@
+'''
+Reference implementation of node2vec. 
+
+Author: Aditya Grover
+
+For more details, refer to the paper:
+node2vec: Scalable Feature Learning for Networks
+Aditya Grover and Jure Leskovec 
+Knowledge Discovery and Data Mining (KDD), 2016
+'''
+import time
+import argparse
+import numpy as np
+import networkx as nx
+import node2vec
+from gensim.models import Word2Vec
+
+
+GRAPH_SIZE = 100
+def parse_args():
+	'''
+	Parses the node2vec arguments.
+	'''
+	print("Parses the node2vec arguments.")
+	parser = argparse.ArgumentParser(description="Run node2vec.")
+	#graph/dataset/data_train.npy
+	parser.add_argument('--input', nargs='?', default='graph/size_val.npy',
+	                    help='Input graph path')
+
+	parser.add_argument('--output', nargs='?', default='emb/karate.emb',
+	                    help='Embeddings path')
+
+	parser.add_argument('--dimensions', type=int, default=6,
+	                    help='Number of dimensions. Default is 180.')
+
+	parser.add_argument('--walk-length', type=int, default=5,
+	                    help='Length of walk per source. Default is 80.')
+
+	parser.add_argument('--num-walks', type=int, default=10,
+	                    help='Number of walks per source. Default is 10.')
+
+	parser.add_argument('--window-size', type=int, default=2,
+                    	help='Context size for optimization. Default is 10.')
+
+	parser.add_argument('--iter', default=1, type=int,
+                      help='Number of epochs in SGD')
+
+	parser.add_argument('--workers', type=int, default=16,
+	                    help='Number of parallel workers. Default is 16.')
+
+	parser.add_argument('--p', type=float, default=1,
+	                    help='Return hyperparameter. Default is 1.')
+
+	parser.add_argument('--q', type=float, default=1,
+	                    help='Inout hyperparameter. Default is 1.')
+
+	parser.add_argument('--weighted', dest='weighted', action='store_true',
+	                    help='Boolean specifying (un)weighted. Default is unweighted.')
+	parser.add_argument('--unweighted', dest='unweighted', action='store_false')
+	parser.set_defaults(weighted=False)
+
+	parser.add_argument('--directed', dest='directed', action='store_true',
+	                    help='Graph is (un)directed. Default is undirected.')
+	parser.add_argument('--undirected', dest='undirected', action='store_false')
+	parser.set_defaults(directed=False)
+
+	return parser.parse_args()
+
+
+def read(arr):
+	G = nx.Graph()
+	for a,b in arr:
+		if not G.has_node(a):
+			G.add_node(a)
+		if not G.has_node(b):
+			G.add_node(b)
+		G.add_edge(a,b,weight=1)
+	return G
+
+def read_all():
+	print("input",args.input)
+	data = np.load(args.input,allow_pickle=True)
+	id=0
+	print(data)
+	for x in data:
+		nx_G=read(x)
+		G = node2vec.Graph(nx_G, args.directed, args.p, args.q)
+		G.preprocess_transition_probs()
+		walks = G.simulate_walks(args.num_walks, args.walk_length)
+		result=learn_embeddings(walks)
+		result = np.asarray(result)
+		name = str(args.input)
+		name = name[:name.index('.')]
+		np.save("C:\\Users\\LENOVO\\PycharmProjects\\walid\\data_val"+"\\transformed_"+str(id),result)
+		print(id,"DONE")
+		id+=1
+	
+
+def read_graph():
+	'''
+	Reads the input network in networkx.
+	'''
+	print("Reads the input network in networkx.")
+	if args.weighted:
+		G = nx.read_edgelist(args.input, nodetype=int, data=(('weight',float),), create_using=nx.DiGraph())
+	else:
+		G = nx.read_edgelist(args.input, nodetype=int, create_using=nx.DiGraph())
+		for edge in G.edges():
+			G[edge[0]][edge[1]]['weight'] = 1
+
+	if not args.directed:
+		G = G.to_undirected()
+
+	return G
+
+def learn_embeddings(walks):
+	'''
+	Learn embeddings by optimizing the Skipgram objective using SGD.
+	'''
+	print("Learn embeddings by optimizing the Skipgram objective using SGD.")
+
+	walks = [list(map(str, walk)) for walk in walks]	
+	model = Word2Vec(walks, size=args.dimensions, window=args.window_size, min_count=0, sg=1, workers=args.workers, iter=args.iter)
+	#model.wv.save_word2vec_format('C:/Users/LENOVO/Desktop/aves-sparrow-social.csv')
+	result = np.zeros((GRAPH_SIZE,int(args.dimensions)))
+	for i in range(len(model.wv.vectors)):
+		key = str(i+1)
+		if key in model.wv.index2entity:
+			result[model.wv.index2entity.index(key)]=model.wv.vectors[model.wv.index2entity.index(key)]
+	if 0 in model.wv.index2entity:
+		result[model.wv.index2entity.index(0)] = model.wv.vectors[model.wv.index2entity.index(0)]
+	result = np.asarray(result)
+	return result
+
+def main(args):
+	'''
+	Pipeline for representational learning for all nodes in a graph.
+	'''
+	print("Pipeline for representational learning for all nodes in a graph.")
+	nx_G = read_graph()
+	G = node2vec.Graph(nx_G, args.directed, args.p, args.q)
+	G.preprocess_transition_probs()
+	walks = G.simulate_walks(args.num_walks, args.walk_length)
+	learn_embeddings(walks)
+
+def main2(args):
+	'''
+	Pipeline for representational learning for all nodes in a graph.
+	'''
+	print("Pipeline for representational learning for all nodes in a graph.")
+	start = time.time()
+	read_all()
+	end = time.time()
+	print("Time",end-start)
+
+if __name__ == "__main__":
+	args = parse_args()
+	import os
+	print(os.getcwd())
+	main2(args)
\ No newline at end of file
diff --git a/PatternRecognition/src/model_train.h5 b/PatternRecognition/src/model_train.h5
new file mode 100644
index 0000000000000000000000000000000000000000..5fa200eafee008d678c89c681ef2dc183595ea50
Binary files /dev/null and b/PatternRecognition/src/model_train.h5 differ
diff --git a/PatternRecognition/src/node2vec.py b/PatternRecognition/src/node2vec.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb5ab66522ac152dfd83113625770fb66b559990
--- /dev/null
+++ b/PatternRecognition/src/node2vec.py
@@ -0,0 +1,146 @@
+import numpy as np
+import networkx as nx
+import random
+
+
+class Graph():
+	def __init__(self, nx_G, is_directed, p, q):
+		self.G = nx_G
+		self.is_directed = is_directed
+		self.p = p
+		self.q = 1
+
+	def node2vec_walk(self, walk_length, start_node):
+		'''
+		Simulate a random walk starting from start node.
+		'''
+		G = self.G
+		alias_nodes = self.alias_nodes
+		alias_edges = self.alias_edges
+
+		walk = [start_node]
+
+		while len(walk) < walk_length:
+			cur = walk[-1]
+			cur_nbrs = sorted(G.neighbors(cur))
+			if len(cur_nbrs) > 0:
+				if len(walk) == 1:
+					walk.append(cur_nbrs[alias_draw(alias_nodes[cur][0], alias_nodes[cur][1])])
+				else:
+					prev = walk[-2]
+					next = cur_nbrs[alias_draw(alias_edges[(prev, cur)][0], 
+						alias_edges[(prev, cur)][1])]
+					walk.append(next)
+			else:
+				break
+		return walk
+
+	def simulate_walks(self, num_walks, walk_length):
+		'''
+		Repeatedly simulate random walks from each node.
+		'''
+		G = self.G
+		walks = []
+		nodes = list(G.nodes())
+		print("Walk iteration:")
+		for walk_iter in range(num_walks):
+			print(str(walk_iter+1), '/', str(num_walks))
+			random.shuffle(nodes)
+			for node in nodes:
+				walks.append(self.node2vec_walk(walk_length=walk_length, start_node=node))
+		return walks
+
+	def get_alias_edge(self, src, dst):
+		'''
+		Get the alias edge setup lists for a given edge.
+		'''
+		G = self.G
+		p = self.p
+		q = self.q
+
+		unnormalized_probs = []
+		for dst_nbr in sorted(G.neighbors(dst)):
+			if dst_nbr == src:
+				unnormalized_probs.append(G[dst][dst_nbr]['weight']/p)
+			elif G.has_edge(dst_nbr, src):
+				unnormalized_probs.append(G[dst][dst_nbr]['weight'])
+			else:
+				unnormalized_probs.append(G[dst][dst_nbr]['weight']/q)
+		norm_const = sum(unnormalized_probs)
+		normalized_probs =  [float(u_prob)/norm_const for u_prob in unnormalized_probs]
+
+		return alias_setup(normalized_probs)
+
+	def preprocess_transition_probs(self):
+		'''
+		Preprocessing of transition probabilities for guiding the random walks.
+		'''
+		G = self.G
+		is_directed = self.is_directed
+		alias_nodes = {}
+		for node in G.nodes():
+			unnormalized_probs = [G[node][nbr]['weight'] for nbr in sorted(G.neighbors(node))]
+			norm_const = sum(unnormalized_probs)
+			normalized_probs =  [float(u_prob)/norm_const for u_prob in unnormalized_probs]
+			alias_nodes[node] = alias_setup(normalized_probs)
+
+		alias_edges = {}
+		triads = {}
+		if is_directed:
+			for edge in G.edges():
+				alias_edges[edge] = self.get_alias_edge(edge[0], edge[1])
+		else:
+			i = 0
+			for edge in G.edges():
+				i = i+1
+				alias_edges[edge] = self.get_alias_edge(edge[0], edge[1])
+				alias_edges[(edge[1], edge[0])] = self.get_alias_edge(edge[1], edge[0])
+		self.alias_nodes = alias_nodes
+		self.alias_edges = alias_edges
+
+		return
+
+
+def alias_setup(probs):
+	'''
+	Compute utility lists for non-uniform sampling from discrete distributions.
+	Refer to https://hips.seas.harvard.edu/blog/2013/03/03/the-alias-method-efficient-sampling-with-many-discrete-outcomes/
+	for details
+	'''
+	K = len(probs)
+	q = np.zeros(K)
+	J = np.zeros(K, dtype=np.int)
+
+	smaller = []
+	larger = []
+	for kk, prob in enumerate(probs):
+	    q[kk] = K*prob
+	    ''' if q[kk] < 1.0:'''
+	    smaller.append(kk)
+	    '''else:
+	        larger.append(kk)
+			'''
+	while len(smaller) > 0 and len(larger) > 0:
+	    small = smaller.pop()
+	    large = larger.pop()
+
+	    J[small] = large
+	    q[large] = q[large] + q[small] - 1.0
+	    if q[large] < 1.0:
+	        smaller.append(large)
+	    else:
+	        larger.append(large)
+
+	return J, q
+
+def alias_draw(J, q):
+	'''
+	Draw data_train from a non-uniform discrete distribution using alias sampling.
+	'''
+	K = len(J)
+
+	kk = int(np.floor(np.random.rand()*K))
+	if np.random.rand() < q[kk]:
+	    return kk
+	else:
+	    return J[kk]
\ No newline at end of file
diff --git a/PatternRecognition/src/patterns.txt b/PatternRecognition/src/patterns.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/PatternRecognition/src/soc-tribes.edges b/PatternRecognition/src/soc-tribes.edges
new file mode 100644
index 0000000000000000000000000000000000000000..df7e57af273969c32c9a13349e69d29bb3b089ae
--- /dev/null
+++ b/PatternRecognition/src/soc-tribes.edges
@@ -0,0 +1,60 @@
+1 2
+1 3
+2 3
+1 4
+3 4
+1 5
+2 5
+1 6
+2 6
+3 6
+3 7
+5 7
+6 7
+3 8
+4 8
+6 8
+7 8
+2 9
+5 9
+6 9
+2 10
+9 10
+6 11
+7 11
+8 11
+9 11
+10 11
+1 12
+6 12
+7 12
+8 12
+11 12
+6 13
+7 13
+9 13
+10 13
+11 13
+5 14
+8 14
+12 14
+13 14
+1 15
+2 15
+5 15
+9 15
+10 15
+11 15
+12 15
+13 15
+1 16
+2 16
+5 16
+6 16
+11 16
+12 16
+13 16
+14 16
+15 16
+17 18
+19 20
\ No newline at end of file
diff --git a/Quasi-clique_generate.py b/Quasi-clique_generate.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1e02a0aab7e58432d05d3a83cdfddb1dc98edd4
--- /dev/null
+++ b/Quasi-clique_generate.py
@@ -0,0 +1,98 @@
+from networkx.generators import community
+from networkx.generators import random_graphs
+from networkx.algorithms import clique
+import networkx as nx
+import random,numpy as np,os
+import copy
+import matplotlib.pyplot as plt
+
+def generate_clique(nb,size,total_size):
+    sub = community.caveman_graph(nb, size)
+    G = random_graphs.fast_gnp_random_graph(total_size, 0.07)  # Generation de graphes aleatoires avec 0.1% de liens
+    G = nx.compose(G, sub)  # fusion des deux graphes, obtention d'un graphe aleatoire avec nb cliques
+    node_mapping = dict(zip(G.nodes(), sorted(G.nodes(), key=lambda k: random.random())))  # creation du mapping
+    G = nx.relabel_nodes(G, node_mapping)
+    cliques = list(clique.find_cliques(G))
+    cliquess = np.asarray(([y for x in cliques for y in x if len(x) >= 4]))
+    for i in range(100):
+        if i not in cliquess:
+            x = list(nx.neighbors(G, i))
+            for k in cliques:
+                v = 0
+                for j in range(len(x)):
+                    if x[j] in k and len(k) > 3:
+                        v += 1
+
+                        if v >= len(k) - 2:
+                            cliquess = np.append(cliquess, [i])
+                            break
+    out = np.zeros(100)
+    out[cliquess] = 1
+
+    return G,out,len(np.unique(cliquess)),cliquess,size
+
+
+
+def to_input_shape(G):# remplissage du fichier .edgelist format noeud -> noeud-voisin
+    tab=[]
+    for a,b in G.edges():
+        tab.append([a,b])
+    return tab
+
+BASE_PATH = "data"
+DIR ="Quasi_clique"
+
+if(not os.path.exists(BASE_PATH)):
+    os.mkdir(BASE_PATH)
+PATH = os.path.join(BASE_PATH,DIR)
+
+if(not os.path.exists(PATH)):
+    os.mkdir(PATH)
+
+total_size = 100
+max_size_clique = 20
+max_clique_count = 5
+outputs = []
+Gr_size = 1000
+graph = []
+data = []
+lab = []
+nodes = []
+input = []
+sz = []
+B = [None]*total_size
+x = 0
+for id in range(Gr_size):
+    G,labels,y,z,s = generate_clique(random.randint(4,max_clique_count),random.randint(4,max_size_clique),total_size)
+    tab = to_input_shape(G)
+    graph.append(tab)
+    A = nx.adjacency_matrix(G, nodelist=range(total_size), weight='weight')
+    A.setdiag(A.diagonal() * 2)
+    A = A.todense()
+    print("la taille : ",s)
+    B = copy.deepcopy(A)
+    input.append(A)
+    for i in range(len(B)):
+        if i not in z:
+            B[i] = 0
+    outputs.append(y)
+    lab.append(labels)
+    data.append(B)
+    sz.append(s)
+    print(id)
+print("graphe",graph[0])
+print("matrix",data[0])
+
+np.save(os.path.join(PATH, "size.npy"), np.asarray(sz))
+np.save(os.path.join(PATH, "data.npy"), np.asarray(graph))
+np.save(os.path.join(PATH, "data2.npy"), np.asarray(data))
+output = np.asarray(outputs)
+np.save(os.path.join(PATH,"output.npy"),output)
+print("out",output[0])
+labs = np.asarray(lab)
+np.save(os.path.join(PATH,"labels2.npy"),labs)
+print("labs",labs[0])
+node = np.asarray(input)
+np.save(os.path.join(PATH,"nodes.npy"),node)
+print("nodes",node[0])
+
diff --git a/Skit-Cluster.py b/Skit-Cluster.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7d6b96cdfaf75eac0f1223e39651d37845c5e89
--- /dev/null
+++ b/Skit-Cluster.py
@@ -0,0 +1,175 @@
+from sknetwork.clustering import Louvain
+from sknetwork.data import karate_club
+import networkx as nx
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy import sparse
+import time
+from sknetwork.clustering import PropagationClustering
+from sknetwork.data import karate_club
+from sknetwork.clustering import modularity
+from collections import Counter
+
+#f = np.load('node2vec/src/graph/test_Star.npy',allow_pickle=True)
+#M = [[1,0,0],[1,0,0],[1,0,0],[1,0,0],[0,1,0],[0,1,0],[0,1,0],[0,1,0],[0,0,1],[0,0,1],[0,0,1],[0,0,1]]
+fh = open("C:/Users/LENOVO/Desktop/karate.edgelist", "rb")
+
+
+j = [[106, 269, 255, 161, 140, 204], [106, 269, 255, 161, 262, 18], [106, 269, 255, 161, 262, 204], [106, 269, 255, 120, 18], [106, 269, 255, 120, 140, 204], [106, 269, 255, 94, 140], [106, 269, 255, 94, 262], [106, 269, 25, 262, 94], [106, 269, 25, 262, 204], [106, 269, 139, 262, 204], [106, 71, 120, 255, 18], [106, 71, 120, 255, 204], [106, 71, 262, 18, 255], [106, 71, 262, 204, 255], [106, 71, 262, 204, 25], [106, 71, 262, 204, 139], [7, 48, 152, 132, 21, 14, 87], [7, 48, 152, 132, 21, 14, 3], [7, 48, 152, 132, 21, 78, 87], [7, 48, 152, 132, 97, 78, 87], [7, 48, 152, 132, 97, 3], [7, 43, 152, 21, 87, 132, 14], [7, 43, 152, 21, 87, 132, 78], [7, 43, 152, 21, 87, 28, 14], [7, 43, 152, 21, 3, 14, 132], [7, 43, 152, 21, 3, 14, 28], [7, 43, 152, 97, 87, 28], [7, 43, 152, 97, 87, 78, 132], [7, 43, 152, 97, 3, 132], [7, 43, 152, 97, 3, 28], [258, 293, 185, 233, 38, 236, 207], [258, 293, 185, 233, 236, 184, 207], [258, 293, 185, 233, 236, 229, 210], [258, 293, 185, 233, 236, 229, 207], [258, 293, 185, 233, 247, 184, 298], [258, 293, 185, 233, 247, 184, 207], [258, 293, 185, 233, 247, 229, 298, 210], [258, 293, 185, 233, 247, 229, 207], [258, 293, 232, 38, 236, 207], [258, 293, 232, 38, 248], [258, 293, 232, 236, 207, 114], [258, 293, 232, 236, 207, 184], [258, 293, 232, 236, 207, 229], [258, 293, 232, 236, 210, 229], [258, 293, 232, 236, 210, 66], [258, 293, 232, 236, 66, 114], [258, 293, 232, 298, 248, 210, 66], [258, 293, 232, 298, 248, 210, 247], [258, 293, 232, 298, 248, 184, 247], [258, 293, 232, 298, 114, 66], [258, 293, 232, 298, 229, 210, 247], [258, 293, 232, 207, 247, 184], [258, 293, 232, 207, 247, 229], [258, 293, 233, 248, 38], [258, 293, 233, 248, 247, 298, 210], [258, 293, 233, 248, 247, 298, 184], [258, 293, 233, 114, 236, 207], [258, 293, 233, 114, 298], [258, 63, 298, 233, 114], [258, 63, 298, 233, 247, 248, 184], [258, 63, 298, 233, 247, 229], [258, 63, 298, 66, 248], [258, 63, 298, 66, 114], [258, 63, 207, 233, 114], [258, 63, 207, 233, 247, 184], [258, 63, 207, 233, 247, 229], [258, 53, 232, 38, 236], [258, 53, 232, 38, 248], [258, 53, 232, 236, 114], [258, 53, 232, 236, 229], [258, 53, 232, 298, 114], [258, 53, 232, 298, 247, 248], [258, 53, 232, 298, 247, 229], [128, 199, 102, 113, 151, 10], [128, 199, 102, 113, 151, 2], [128, 199, 102, 113, 151, 56], [128, 199, 102, 113, 84, 10], [128, 199, 102, 113, 84, 2], [128, 199, 102, 113, 84, 56], [128, 199, 102, 6, 151, 56], [128, 199, 153, 84, 113, 11, 10], [128, 199, 153, 84, 113, 2], [128, 199, 153, 84, 113, 56], [128, 199, 153, 84, 31, 10], [128, 199, 153, 84, 31, 56], [128, 199, 153, 6, 56, 31], [128, 199, 151, 11, 10, 113], [128, 199, 151, 31, 10], [128, 199, 151, 31, 56, 6], [128, 173, 102, 113, 151, 10], [128, 173, 102, 113, 151, 2], [128, 173, 102, 113, 151, 56], [128, 173, 102, 113, 84, 10], [128, 173, 102, 113, 84, 2], [128, 173, 102, 113, 84, 56], [128, 173, 102, 6, 151, 56], [128, 173, 153, 84, 113, 11, 10], [128, 173, 153, 84, 113, 2], [128, 173, 153, 84, 113, 56], [128, 173, 153, 84, 31, 10], [128, 173, 153, 84, 31, 56], [128, 173, 153, 6, 56, 31], [128, 173, 151, 11, 10, 113], [128, 173, 151, 31, 10], [128, 173, 151, 31, 56, 6], [68, 243, 51, 12], [68, 243, 51, 75], [68, 243, 81, 167], [68, 243, 81, 72], [68, 243, 81, 188], [68, 243, 75, 167], [68, 23, 188, 267], [68, 23, 75, 51], [68, 23, 75, 167], [68, 267, 81, 72], [68, 267, 81, 188], [68, 267, 54, 72], [65, 199, 102, 2, 84], [65, 199, 102, 2, 151], [65, 199, 102, 56, 84], [65, 199, 102, 56, 151, 6], [65, 199, 153, 29, 11], [65, 199, 153, 29, 2], [65, 199, 153, 29, 6], [65, 199, 153, 84, 11], [65, 199, 153, 84, 2], [65, 199, 153, 84, 56, 31], [65, 199, 153, 6, 56, 31], [65, 199, 151, 11, 29], [65, 199, 151, 2, 29], [65, 199, 151, 6, 29], [65, 199, 151, 6, 31, 56], [148, 70, 168, 155, 270], [148, 70, 168, 155, 201], [148, 70, 228, 270], [148, 70, 228, 201], [148, 289, 55, 270, 155, 168], [148, 289, 55, 270, 228], [148, 289, 291, 73, 228, 220], [148, 289, 291, 73, 228, 91], [148, 289, 291, 168, 270, 155], [148, 289, 291, 168, 270, 220], [148, 289, 291, 168, 270, 91], [148, 289, 291, 168, 201, 155], [148, 289, 291, 168, 201, 220], [148, 289, 291, 228, 270, 220], [148, 289, 291, 228, 270, 91], [148, 289, 291, 228, 201, 220], [73, 291, 278, 163, 289, 93], [73, 291, 278, 163, 289, 220], [73, 291, 278, 163, 289, 91], [73, 291, 278, 163, 26, 220], [73, 291, 278, 163, 26, 91], [73, 291, 278, 27, 220], [73, 291, 228, 289, 93], [73, 291, 228, 26, 220], [73, 291, 228, 26, 91], [110, 255, 269, 103, 140], [110, 255, 269, 103, 262], [110, 255, 269, 187, 140, 120, 204], [110, 255, 269, 187, 140, 94], [110, 255, 269, 187, 262, 94], [110, 255, 269, 187, 262, 204], [110, 255, 143, 187, 120, 204], [110, 255, 143, 187, 262, 94], [110, 255, 143, 187, 262, 204], [110, 255, 71, 204, 120], [110, 255, 71, 204, 262], [110, 139, 262, 103, 269], [110, 139, 262, 204, 187, 269], [110, 139, 262, 204, 187, 143], [110, 139, 262, 204, 71], [254, 158, 230, 272, 39, 259], [254, 158, 230, 272, 250, 259], [254, 158, 230, 272, 250, 118], [254, 158, 230, 272, 250, 166], [254, 158, 230, 186, 259], [254, 158, 230, 186, 118], [254, 158, 230, 186, 166], [254, 295, 116, 231, 39, 259], [254, 295, 116, 231, 4], [254, 295, 116, 231, 118], [254, 295, 116, 250, 259], [254, 295, 116, 250, 118], [254, 295, 272, 33, 159], [254, 295, 272, 33, 118, 250], [254, 295, 272, 230, 159, 166], [254, 295, 272, 230, 231, 39, 259], [254, 295, 272, 230, 231, 118], [254, 295, 272, 230, 231, 166], [254, 295, 272, 230, 250, 259], [254, 295, 272, 230, 250, 118], [254, 295, 272, 230, 250, 166], [254, 295, 272, 4, 159], [254, 295, 272, 4, 231], [254, 295, 186, 230, 159, 166], [254, 295, 186, 230, 231, 259], [254, 295, 186, 230, 231, 118], [254, 295, 186, 230, 231, 166], [254, 295, 186, 4, 159], [254, 295, 186, 4, 231], [254, 109, 159, 4], [254, 109, 159, 166], [254, 109, 231, 259, 39], [254, 109, 231, 259, 86], [254, 109, 231, 4], [254, 109, 231, 166, 86], [254, 109, 250, 86, 259], [254, 109, 250, 86, 166], [254, 86, 231, 116, 259], [254, 86, 231, 116, 118], [254, 86, 231, 272, 259], [254, 86, 231, 272, 118], [254, 86, 231, 272, 166], [254, 86, 250, 116, 259], [254, 86, 250, 116, 118], [254, 86, 250, 272, 259], [254, 86, 250, 272, 33, 118], [254, 86, 250, 272, 166], [222, 135, 47, 243], [222, 135, 47, 267], [222, 135, 190, 104, 182], [222, 135, 190, 200, 243], [222, 135, 190, 200, 267, 182], [222, 135, 190, 200, 267, 99], [222, 157, 200, 167, 243], [222, 157, 200, 167, 182], [222, 157, 200, 190, 12, 243], [222, 157, 200, 190, 12, 267], [222, 157, 200, 190, 182, 267], [222, 200, 188, 243], [222, 200, 188, 182, 267], [222, 200, 99, 12, 267, 190], [222, 200, 99, 167], [222, 23, 182, 167, 75], [222, 23, 182, 267, 188], [222, 23, 182, 267, 190], [222, 23, 99, 167, 75], [222, 23, 99, 267, 190], [222, 246, 243, 12, 190], [222, 246, 243, 167, 75], [222, 246, 243, 167, 47], [222, 246, 243, 188, 47], [222, 246, 99, 12, 267, 190], [222, 246, 99, 167, 75], [222, 246, 104, 12, 190], [222, 246, 104, 182, 75], [222, 246, 104, 182, 188], [222, 246, 104, 182, 190], [222, 246, 267, 188, 182], [222, 246, 267, 188, 47], [222, 246, 267, 190, 182], [222, 246, 182, 167, 75], [201, 70, 252, 278, 155, 168, 176], [201, 70, 252, 278, 155, 168, 27], [201, 70, 252, 228, 176], [201, 291, 252, 155, 278, 168, 176, 289], [201, 291, 252, 155, 278, 168, 27], [201, 291, 252, 220, 289, 176, 278, 168], [201, 291, 252, 220, 289, 176, 228], [201, 291, 252, 220, 289, 17, 228], [201, 291, 252, 220, 27, 278, 168], [201, 291, 252, 220, 27, 17], [201, 291, 163, 278, 289, 155], [201, 291, 163, 278, 289, 220], [26, 291, 278, 176, 252, 220], [26, 291, 278, 176, 91], [26, 291, 278, 270, 252, 220], [26, 291, 278, 270, 163, 220], [26, 291, 278, 270, 163, 91], [26, 291, 228, 252, 220, 176], [26, 291, 228, 252, 220, 270], [26, 291, 228, 91, 176], [26, 291, 228, 91, 270], [72, 243, 215, 47, 246, 81], [72, 243, 215, 47, 135], [72, 243, 200, 135], [72, 267, 130, 215, 246, 81], [72, 267, 130, 215, 135], [72, 267, 130, 200, 135], [72, 267, 54, 246], [72, 267, 54, 135, 200], [72, 267, 47, 215, 246, 81], [72, 267, 47, 215, 135], [218, 112, 251, 178], [218, 112, 251, 181], [218, 112, 251, 195], [218, 112, 145, 178], [218, 46, 178, 297], [218, 46, 178, 145], [218, 46, 257, 297], [218, 46, 257, 181], [218, 178, 297, 251, 227], [218, 178, 297, 251, 194], [218, 178, 297, 149], [218, 178, 145, 227], [218, 178, 145, 194], [218, 178, 145, 149], [218, 244, 257, 191, 227, 198], [218, 244, 257, 297, 251, 227], [218, 244, 257, 297, 251, 194], [218, 244, 257, 297, 198, 227], [218, 244, 257, 297, 198, 194], [218, 244, 149, 191], [218, 244, 149, 297], [218, 244, 149, 145], [218, 244, 145, 227], [218, 244, 145, 194], [218, 181, 251, 257, 227], [218, 181, 191, 257, 227, 198], [218, 181, 191, 149], [218, 195, 251, 194], [14, 22, 152, 87, 132, 48], [14, 22, 152, 87, 28], [14, 22, 152, 3, 132, 48], [14, 22, 152, 3, 28], [96, 189, 183, 192], [96, 189, 234, 137], [96, 189, 234, 192], [96, 189, 234, 122], [96, 189, 279, 192], [96, 189, 279, 122], [96, 180, 137, 234], [96, 273, 169, 183], [96, 273, 169, 234, 122], [96, 273, 277, 137, 234], [96, 273, 277, 137, 177], [96, 273, 277, 192, 183], [96, 273, 277, 192, 234], [96, 273, 277, 192, 177, 279], [96, 273, 122, 279], [184, 263, 232, 126, 236], [184, 263, 232, 126, 298, 248], [184, 263, 232, 207, 236], [184, 263, 233, 236, 126], [184, 263, 233, 236, 207], [184, 263, 233, 248, 298, 63], [184, 263, 233, 248, 298, 126], [184, 263, 233, 248, 298, 133], [184, 263, 233, 207, 63], [184, 263, 233, 207, 133], [184, 126, 293, 236, 232], [184, 126, 293, 236, 233], [184, 126, 293, 248, 298, 247, 232], [184, 126, 293, 248, 298, 247, 233], [184, 133, 247, 293, 233, 298, 248], [184, 133, 247, 293, 233, 207], [260, 105, 209, 287], [260, 105, 209, 0], [260, 105, 209, 241], [260, 105, 36, 287], [260, 249, 107, 285], [260, 249, 107, 36, 287], [260, 249, 107, 36, 286], [260, 249, 175, 285], [260, 249, 175, 129], [260, 249, 175, 125], [260, 249, 9, 285], [260, 249, 241, 286], [260, 249, 241, 129], [260, 249, 125, 287], [260, 249, 125, 286], [260, 249, 129, 287], [260, 8, 241, 286], [260, 8, 241, 129], [260, 8, 0, 286], [260, 8, 286, 125], [260, 8, 175, 285], [260, 8, 175, 129], [260, 8, 175, 125], [260, 209, 107, 285], [260, 209, 107, 287], [260, 209, 107, 286], [260, 209, 241, 286], [260, 209, 0, 286], [260, 209, 175, 285], [260, 136, 285, 175], [260, 136, 285, 242], [260, 136, 286, 241], [260, 136, 129, 175], [260, 136, 129, 242, 287], [260, 136, 129, 242, 241], [260, 242, 125, 287], [3, 21, 1, 43, 152, 132], [3, 21, 1, 43, 152, 28], [3, 97, 152, 22, 1, 132], [3, 97, 152, 22, 1, 28], [3, 97, 152, 22, 48, 132], [3, 97, 152, 1, 43, 132], [3, 97, 152, 1, 43, 28], [67, 282, 245, 256, 266, 115, 13], [67, 282, 245, 256, 266, 205, 174], [67, 282, 245, 256, 266, 205, 150], [67, 282, 245, 256, 266, 205, 13], [67, 282, 245, 256, 90, 115, 13], [67, 282, 245, 256, 90, 174], [67, 282, 245, 256, 90, 150], [58, 251, 100, 211], [58, 251, 221, 195, 112], [58, 251, 221, 181, 79], [58, 251, 221, 181, 112], [58, 251, 211, 181], [58, 251, 211, 244], [58, 251, 211, 195], [58, 98, 244, 211], [58, 98, 195, 221, 112], [58, 98, 195, 211], [58, 149, 221, 181], [58, 149, 221, 195], [154, 195, 46, 211], [154, 195, 211, 82, 226], [154, 195, 211, 98], [154, 195, 265, 82, 221], [154, 195, 265, 82, 226], [154, 195, 265, 98, 221], [154, 297, 221, 32], [154, 297, 221, 98, 265], [154, 297, 257, 46], [154, 297, 257, 32], [154, 297, 257, 227, 226], [154, 297, 257, 227, 244], [154, 297, 257, 265, 226], [154, 297, 257, 265, 98, 244], [154, 297, 211, 46], [154, 297, 211, 226, 227], [154, 297, 211, 244, 227], [154, 297, 211, 244, 98], [154, 297, 271, 244], [154, 82, 227, 211, 226], [154, 82, 227, 211, 244], [154, 82, 244, 265], [200, 51, 157, 12, 243], [200, 51, 157, 54, 182], [200, 51, 157, 130], [200, 130, 267, 188], [200, 130, 267, 190, 135], [200, 130, 267, 190, 157], [200, 54, 182, 135, 267], [200, 54, 182, 157, 167], [200, 54, 182, 157, 267], [88, 125, 287, 242], [88, 125, 175, 238], [88, 284, 285, 127, 242], [88, 284, 285, 175, 238], [88, 284, 287, 242], [88, 284, 286, 241], [88, 284, 241, 242], [88, 284, 241, 238], [212, 299, 280, 219, 50, 16], [212, 299, 280, 219, 50, 290], [212, 299, 280, 264, 45], [212, 299, 280, 264, 172, 16], [212, 299, 280, 264, 172, 290], [212, 299, 280, 264, 101], [212, 299, 283, 141, 219, 290], [212, 299, 283, 141, 101], [212, 299, 283, 219, 50, 16], [212, 299, 283, 219, 50, 290], [212, 299, 283, 264, 45], [212, 299, 283, 264, 172, 16], [212, 299, 283, 264, 172, 290], [212, 299, 283, 264, 101], [212, 299, 124, 219], [212, 299, 124, 264, 101], [212, 299, 124, 264, 172], [212, 144, 280, 119, 45], [212, 144, 280, 119, 290], [212, 144, 280, 264, 45], [212, 144, 280, 264, 172, 16], [212, 144, 280, 264, 172, 290], [212, 144, 280, 264, 101], [212, 119, 280, 219, 290], [212, 119, 283, 219, 290], [212, 119, 283, 45], [78, 21, 1, 43, 152, 132, 87], [78, 97, 87, 132, 152, 22, 1], [78, 97, 87, 132, 152, 22, 48], [78, 97, 87, 132, 152, 1, 43], [242, 57, 241, 136], [242, 57, 241, 121], [242, 57, 125, 121], [242, 57, 125, 170], [242, 57, 170, 136], [242, 125, 287, 121], [242, 125, 287, 170], [242, 284, 59, 285, 127], [242, 284, 59, 287, 170], [242, 284, 239, 285, 127], [242, 284, 239, 129, 287], [242, 284, 239, 129, 127], [242, 284, 239, 129, 241], [242, 284, 239, 170, 287], [242, 136, 59, 285], [242, 136, 59, 287, 170], [242, 117, 239, 285], [242, 117, 239, 287, 170], [51, 75, 182, 23], [193, 40, 169, 138], [193, 40, 169, 216], [193, 40, 177, 138, 137], [193, 40, 177, 192], [193, 40, 177, 216], [193, 40, 146, 192], [193, 40, 146, 216], [193, 40, 189, 138, 137], [193, 40, 189, 192], [193, 40, 189, 216], [193, 146, 277, 279, 192, 162], [193, 146, 277, 279, 216], [193, 296, 275, 137, 189, 138], [193, 296, 275, 137, 277, 138, 177], [193, 296, 275, 137, 277, 162], [193, 296, 275, 169, 138], [193, 296, 275, 169, 216], [193, 296, 275, 169, 162, 122], [193, 296, 275, 279, 189, 138], [193, 296, 275, 279, 189, 216], [193, 296, 275, 279, 189, 122], [193, 296, 275, 279, 277, 162], [193, 296, 275, 279, 277, 177, 138], [193, 296, 275, 279, 277, 177, 216], [193, 296, 275, 279, 122, 162], [193, 296, 192, 279, 189], [193, 296, 192, 279, 277, 162], [193, 296, 192, 279, 277, 177], [267, 81, 215, 188, 246, 130], [267, 81, 215, 188, 246, 47], [267, 81, 215, 157, 130], [267, 130, 215, 135, 190], [267, 130, 215, 190, 246], [267, 130, 215, 190, 157], [267, 197, 268, 61], [267, 215, 23, 188], [267, 215, 23, 99, 190], [267, 215, 99, 190, 246], [267, 215, 99, 190, 135], [267, 54, 246, 182], [204, 140, 269, 187, 255, 161], [204, 140, 269, 187, 255, 206], [204, 262, 269, 187, 161, 255], [204, 262, 269, 187, 206, 255], [204, 262, 269, 187, 206, 139], [204, 262, 269, 69, 161, 255], [204, 262, 269, 69, 25], [204, 262, 269, 69, 206, 255], [204, 262, 269, 69, 206, 139], [204, 262, 143, 187, 161, 255], [204, 262, 143, 187, 206, 255], [204, 262, 143, 187, 206, 139], [204, 262, 143, 69, 161, 255], [204, 262, 143, 69, 25], [204, 262, 143, 69, 206, 255], [204, 262, 143, 69, 206, 139], [204, 262, 71, 206, 255], [204, 262, 71, 206, 139], [36, 287, 203, 105], [36, 287, 284, 59], [36, 287, 284, 249], [36, 238, 284, 249], [36, 286, 249, 284], [71, 213, 120, 255, 18], [71, 213, 262, 25], [71, 213, 262, 206, 255, 18], [71, 213, 262, 206, 139], [114, 263, 236, 232, 66], [114, 263, 236, 232, 53], [114, 263, 236, 232, 207], [114, 263, 236, 233, 207], [114, 263, 236, 20, 207], [114, 263, 298, 63, 233], [114, 263, 298, 63, 66], [114, 263, 298, 232, 66], [114, 263, 298, 232, 53], [114, 263, 298, 20], [114, 263, 207, 63, 233], [114, 293, 20, 236, 207], [114, 293, 20, 298], [114, 293, 245, 233], [25, 269, 213, 44], [25, 269, 213, 94, 262], [25, 143, 262, 94], [159, 295, 208, 33], [159, 295, 208, 186, 123], [159, 295, 208, 186, 230], [159, 295, 208, 186, 4], [159, 295, 253, 272, 33], [159, 295, 253, 272, 4], [159, 295, 253, 272, 166, 123], [159, 295, 253, 272, 166, 42, 230], [159, 295, 186, 42, 230, 166], [159, 295, 186, 166, 123], [159, 109, 208, 4], [159, 109, 253, 42, 166], [159, 109, 253, 4], [16, 77, 299, 219], [16, 77, 299, 172], [16, 77, 144, 172], [290, 77, 141, 219, 299], [290, 77, 141, 108, 160, 111], [290, 77, 141, 108, 299], [290, 77, 119, 219], [290, 77, 119, 108], [290, 77, 119, 144], [290, 77, 172, 299], [290, 77, 172, 144], [290, 77, 144, 160], [290, 141, 281, 299, 283, 219], [290, 141, 281, 299, 283, 108], [290, 141, 281, 111, 108], [290, 141, 160, 283, 108], [290, 141, 160, 111, 131], [290, 141, 131, 219, 299], [290, 144, 280, 160, 264], [290, 259, 108, 264], [290, 264, 281, 299, 280], [290, 264, 281, 299, 108, 283], [290, 264, 281, 111, 280], [290, 264, 281, 111, 108], [290, 264, 160, 283, 108], [290, 264, 160, 111, 280], [290, 264, 160, 111, 108], [290, 288, 131, 160, 111], [290, 288, 131, 50, 219, 299], [290, 288, 280, 160, 111], [290, 288, 280, 119, 219], [290, 288, 280, 299, 219, 281], [290, 288, 280, 299, 219, 50], [290, 288, 280, 299, 172], [290, 288, 280, 111, 281], [290, 288, 283, 160, 108], [290, 288, 283, 119, 219], [290, 288, 283, 119, 108], [290, 288, 283, 299, 281, 219], [290, 288, 283, 299, 281, 108], [290, 288, 283, 299, 172], [290, 288, 283, 299, 50, 219], [290, 288, 283, 37, 219, 281], [290, 288, 283, 37, 219, 50], [290, 288, 283, 37, 172], [290, 288, 111, 281, 108], [290, 288, 111, 281, 37], [290, 288, 111, 160, 108], [103, 269, 161, 213, 255, 140], [103, 269, 161, 213, 255, 262], [103, 269, 161, 213, 44, 140], [103, 269, 161, 69, 255, 262], [103, 269, 206, 213, 255, 140], [103, 269, 206, 213, 255, 262], [103, 269, 206, 213, 139, 44], [103, 269, 206, 213, 139, 262], [103, 269, 206, 213, 44, 140], [103, 269, 206, 69, 262, 255], [103, 269, 206, 69, 262, 139], [249, 175, 238, 284, 285], [249, 175, 238, 284, 129], [249, 175, 238, 125], [249, 57, 286, 241], [249, 57, 286, 125], [249, 239, 107, 285, 127], [249, 239, 107, 287], [249, 239, 107, 286], [249, 239, 284, 285, 238], [249, 239, 284, 285, 9, 127], [249, 239, 284, 286, 241], [249, 239, 284, 129, 287], [249, 239, 284, 129, 127], [249, 239, 284, 129, 241, 238], [279, 35, 275, 180, 138], [279, 35, 275, 180, 214], [279, 35, 275, 202, 138], [279, 35, 275, 202, 273], [279, 35, 275, 273, 214], [279, 35, 192, 273], [279, 146, 180, 216, 214], [279, 146, 180, 162], [279, 146, 277, 273, 192, 162], [279, 146, 277, 273, 216, 214], [279, 146, 277, 273, 216, 202], [279, 296, 275, 180, 138], [279, 296, 275, 180, 162], [279, 296, 275, 180, 214, 216], [279, 296, 275, 189, 214, 216], [279, 296, 275, 189, 214, 122], [279, 296, 275, 189, 202, 138], [279, 296, 275, 189, 202, 216], [279, 296, 275, 189, 202, 122], [279, 296, 275, 277, 138, 202, 177], [279, 296, 275, 277, 273, 216, 177, 214], [279, 296, 275, 277, 273, 216, 177, 202], [279, 296, 275, 277, 273, 162], [279, 296, 275, 122, 273, 214], [279, 296, 275, 122, 273, 162], [279, 296, 275, 122, 273, 202], [279, 296, 192, 273, 277, 162], [279, 296, 192, 273, 277, 177], [35, 234, 275, 214, 180], [35, 234, 275, 214, 273, 169], [35, 234, 275, 202, 273], [35, 234, 192, 273], [35, 138, 169, 275], [295, 42, 116, 259, 250], [295, 42, 230, 186, 259], [295, 42, 230, 250, 253, 272, 259], [295, 42, 230, 250, 253, 272, 166], [295, 208, 33, 250], [295, 208, 33, 80], [295, 208, 231, 259, 123, 116], [295, 208, 231, 259, 123, 186], [295, 208, 231, 259, 116, 39], [295, 208, 231, 259, 116, 80], [295, 208, 231, 259, 230, 39], [295, 208, 231, 259, 230, 186, 80], [295, 208, 231, 4, 116], [295, 208, 231, 4, 186], [295, 208, 250, 259, 123, 116], [295, 208, 250, 259, 230], [295, 80, 272, 33, 118], [295, 80, 272, 230, 231, 259], [295, 80, 272, 230, 231, 118], [295, 80, 118, 231, 116], [295, 80, 118, 231, 230, 186], [295, 123, 231, 259, 253, 272], [295, 123, 231, 166, 253, 272], [295, 123, 231, 166, 186], [295, 123, 250, 259, 253, 272], [295, 123, 250, 166, 253, 272], [295, 253, 272, 33, 118, 250], [295, 253, 272, 230, 231, 39, 259], [295, 253, 272, 230, 231, 118], [295, 253, 272, 230, 231, 166], [295, 253, 272, 230, 250, 118], [295, 253, 272, 4, 231], [109, 259, 208, 39, 231], [109, 259, 208, 250], [109, 259, 253, 39, 231], [109, 259, 253, 231, 86], [109, 259, 253, 250, 42], [109, 259, 253, 250, 86], [109, 166, 253, 231, 86], [109, 166, 253, 250, 42], [109, 166, 253, 250, 86], [109, 4, 231, 208], [109, 4, 231, 253], [205, 235, 282, 266, 237, 245, 256, 217, 174, 52], [205, 235, 282, 266, 237, 245, 256, 217, 174, 5], [205, 235, 282, 266, 237, 245, 256, 217, 150], [205, 235, 282, 266, 237, 245, 256, 13], [205, 235, 282, 266, 237, 245, 256, 134, 52, 174], [205, 235, 282, 266, 237, 245, 256, 134, 150], [205, 235, 282, 266, 19, 217, 5], [205, 235, 282, 266, 19, 217, 150], [205, 235, 282, 266, 19, 134, 150], [140, 269, 187, 213, 255, 161], [140, 269, 187, 213, 255, 120], [140, 269, 187, 213, 255, 94, 206], [140, 269, 187, 213, 44, 161], [140, 269, 187, 213, 44, 120], [140, 269, 187, 213, 44, 206], [296, 183, 192, 189], [296, 183, 192, 277, 273], [296, 183, 216, 214, 180], [296, 183, 216, 214, 273, 277], [296, 183, 216, 214, 273, 169], [296, 183, 216, 214, 189], [296, 183, 216, 202, 273, 277], [296, 183, 216, 202, 189], [296, 275, 137, 138, 180], [296, 275, 137, 234, 162, 180], [296, 275, 137, 234, 162, 273, 277], [296, 275, 137, 234, 189], [296, 275, 137, 177, 273, 277], [296, 275, 234, 180, 214], [296, 275, 234, 277, 273, 214], [296, 275, 234, 277, 273, 202], [296, 275, 234, 122, 273, 202], [296, 275, 234, 122, 273, 169, 214], [296, 275, 234, 122, 273, 169, 162], [296, 275, 234, 122, 189, 214], [296, 275, 234, 122, 189, 202], [296, 275, 169, 216, 214, 273], [296, 192, 234, 162, 273, 277], [296, 192, 234, 189], [255, 150, 19, 282], [255, 262, 269, 161, 18, 213], [255, 262, 269, 161, 18, 69], [255, 262, 269, 161, 187, 213], [255, 262, 269, 206, 213, 18], [255, 262, 269, 206, 213, 187, 94], [255, 262, 269, 206, 69, 18], [255, 262, 143, 94, 187, 206], [255, 120, 269, 213, 18], [143, 187, 44, 161], [143, 187, 44, 139, 206], [143, 187, 44, 120], [39, 158, 230, 259, 208], [39, 158, 230, 259, 272, 253], [151, 29, 173, 113, 11, 10], [151, 29, 173, 113, 2], [151, 29, 173, 6], [151, 29, 199, 113, 11, 10], [151, 29, 199, 113, 2], [10, 11, 153, 29, 113, 173], [10, 11, 153, 29, 113, 199], [135, 243, 190, 215], [135, 104, 54, 182], [135, 104, 190, 130, 215], [61, 268, 164, 225, 271], [61, 268, 274, 197, 225, 271], [61, 268, 274, 223], [61, 294, 147, 276, 223], [61, 294, 147, 276, 271], [61, 294, 223, 274], [61, 294, 225, 62, 276], [61, 294, 225, 62, 274], [61, 294, 225, 271, 276], [61, 294, 225, 271, 164], [61, 294, 225, 271, 197, 274], [150, 235, 282, 217, 237, 245, 256, 266, 49], [150, 235, 282, 217, 237, 245, 256, 90], [150, 235, 282, 217, 19, 266, 49], [282, 235, 256, 237, 245, 266, 115, 217], [282, 235, 256, 237, 245, 266, 115, 13], [282, 235, 256, 237, 245, 266, 115, 134], [282, 235, 256, 237, 245, 266, 49, 217, 52, 174], [282, 235, 256, 237, 245, 90, 217, 115], [282, 235, 256, 237, 245, 90, 217, 174], [282, 235, 256, 237, 245, 90, 13, 115], [282, 235, 19, 266, 217, 115], [282, 235, 19, 266, 134, 115], [20, 248, 298, 210, 263], [20, 248, 298, 210, 293], [20, 229, 185, 293, 236, 210], [20, 229, 185, 293, 236, 207], [20, 229, 185, 293, 298, 210], [20, 229, 263, 236, 210], [20, 229, 263, 236, 207], [20, 229, 263, 298, 210], [294, 276, 223, 92], [294, 276, 62, 92], [294, 276, 240, 60, 147], [294, 276, 240, 60, 142], [294, 276, 240, 142, 225], [294, 276, 240, 271, 147], [294, 276, 240, 271, 165], [294, 276, 240, 271, 225], [294, 276, 92, 60], [294, 276, 92, 165, 271], [294, 147, 196, 223], [294, 147, 196, 240, 60], [294, 147, 196, 240, 271], [294, 147, 171, 60, 240], [294, 147, 171, 223], [294, 274, 223, 196], [294, 274, 223, 171], [294, 274, 34, 62], [294, 274, 34, 271, 89], [294, 274, 62, 171, 225], [294, 274, 240, 60, 196], [294, 274, 240, 60, 171], [294, 274, 240, 165, 197, 196, 271], [294, 274, 240, 165, 171], [294, 274, 240, 225, 197, 271], [294, 274, 240, 225, 171], [294, 274, 89, 197, 271], [294, 164, 142, 225], [294, 164, 271, 165, 196], [294, 164, 271, 89], [294, 142, 197, 225, 240], [294, 92, 171, 60], [294, 92, 171, 223], [294, 92, 171, 62], [294, 92, 171, 165], [294, 92, 89, 271], [92, 179, 60, 276], [92, 179, 60, 171], [92, 179, 223, 276], [92, 179, 223, 268], [92, 179, 223, 171], [92, 179, 89, 268], [92, 268, 165, 271], [92, 268, 271, 89], [169, 40, 183, 216], [241, 238, 209, 284], [241, 238, 129, 136], [241, 238, 129, 8], [241, 286, 209, 284], [241, 286, 136, 57], [55, 270, 289, 278, 252, 93, 156], [55, 270, 289, 278, 252, 155, 168], [55, 270, 289, 278, 163, 93, 156], [55, 270, 289, 278, 163, 155], [55, 270, 289, 228, 252, 93, 156], [160, 45, 264, 144, 280], [160, 45, 264, 283], [160, 101, 283, 141], [160, 101, 283, 264], [160, 101, 283, 288], [160, 101, 280, 264, 144], [160, 101, 280, 288], [82, 100, 149, 265], [82, 194, 226, 83], [82, 194, 226, 195], [82, 149, 265, 221, 195], [82, 149, 265, 244], [82, 83, 226, 265], [194, 145, 226, 178, 83], [194, 297, 178, 226, 83], [194, 297, 178, 98], [194, 297, 100, 251], [194, 297, 100, 198], [194, 297, 257, 226, 198], [194, 297, 257, 244, 98], [185, 15, 293, 38], [185, 15, 293, 298, 247], [40, 183, 146, 192], [40, 183, 146, 216], [40, 183, 189, 192], [40, 183, 189, 216], [172, 76, 264, 74], [172, 76, 264, 144], [172, 74, 299, 264, 280], [172, 74, 299, 264, 124], [172, 124, 299, 288], [244, 191, 198, 257, 265], [244, 191, 198, 211, 227], [244, 191, 149, 265], [244, 145, 265, 149], [244, 297, 251, 227, 211], [244, 297, 198, 257, 265], [244, 297, 198, 211, 227], [244, 297, 149, 265], [99, 167, 215, 246], [99, 167, 215, 23], [176, 252, 278, 93, 156, 289, 291], [176, 252, 278, 156, 70], [176, 252, 228, 93, 156, 289, 291], [176, 252, 228, 156, 70], [176, 91, 291, 289, 278, 156], [176, 91, 291, 289, 278, 168], [176, 91, 291, 289, 228, 156], [127, 285, 30, 107], [127, 285, 30, 59], [127, 285, 209, 107], [127, 285, 209, 284, 59], [149, 265, 100, 191], [149, 265, 100, 297], [149, 265, 221, 178, 297], [149, 265, 221, 178, 145], [149, 265, 220, 195], [286, 107, 117, 30], [286, 107, 117, 239], [286, 203, 170, 57, 125], [286, 203, 170, 117], [286, 117, 170, 239], [286, 170, 57, 136], [286, 170, 284, 239], [286, 0, 284, 239], [286, 0, 284, 209], [139, 269, 187, 206, 213, 44], [139, 269, 187, 206, 213, 262], [53, 15, 248, 38], [53, 15, 248, 298, 263], [53, 15, 248, 298, 247], [53, 232, 263, 248, 298], [53, 232, 263, 229, 236], [53, 232, 263, 229, 298], [208, 158, 259, 123, 186], [208, 158, 259, 123, 250], [208, 158, 259, 230, 186, 80], [208, 158, 259, 230, 250], [234, 146, 180, 162], [234, 146, 180, 214], [234, 146, 277, 273, 192, 162], [234, 146, 277, 273, 214], [234, 146, 277, 273, 202], [226, 265, 83, 178, 297], [226, 265, 83, 178, 112, 145], [226, 265, 83, 191, 79], [226, 265, 257, 198, 191, 79], [226, 265, 257, 198, 297], [226, 265, 195, 112], [226, 227, 178, 297], [226, 227, 178, 145], [226, 227, 198, 191, 257, 79], [226, 227, 198, 191, 211], [226, 227, 198, 297, 257], [226, 227, 198, 297, 211], [156, 270, 70, 252, 278], [156, 270, 70, 252, 228], [156, 270, 291, 289, 93, 252, 278], [156, 270, 291, 289, 93, 252, 17, 228], [156, 270, 291, 289, 93, 163, 278], [156, 270, 291, 289, 91, 278, 163], [156, 270, 291, 289, 91, 228], [144, 95, 45, 119], [144, 76, 45, 264], [182, 54, 104, 246], [182, 54, 246, 167], [123, 158, 186, 166], [123, 158, 250, 253, 272, 259], [123, 158, 250, 253, 272, 166], [116, 86, 80, 231, 259], [116, 86, 80, 231, 118], [60, 179, 147, 276], [60, 179, 147, 196], [60, 179, 147, 171], [60, 85, 276, 142], [60, 85, 171, 274], [220, 291, 270, 289, 252, 278, 168], [220, 291, 270, 289, 252, 17, 228], [220, 291, 270, 289, 163, 278], [107, 287, 117, 239], [107, 285, 117, 30], [107, 285, 117, 239], [281, 59, 284, 285], [281, 111, 24, 288, 280], [281, 111, 24, 288, 95], [281, 111, 95, 37, 288], [281, 285, 239, 284], [281, 283, 95, 45], [281, 283, 95, 288, 24], [281, 283, 95, 288, 37], [281, 283, 299, 45, 264], [281, 283, 299, 101, 141], [281, 283, 299, 101, 264], [281, 283, 299, 101, 288], [281, 76, 264, 74], [281, 76, 264, 45], [281, 76, 264, 108], [281, 124, 24, 95, 288], [281, 124, 299, 219, 288], [281, 124, 299, 101, 74, 264], [281, 124, 299, 101, 288], [281, 74, 299, 101, 141], [281, 74, 299, 101, 280, 264], [281, 280, 299, 45, 264], [281, 280, 299, 101, 288], [291, 289, 270, 155, 278, 252, 168], [291, 289, 270, 155, 278, 163], [291, 289, 270, 168, 278, 91], [133, 298, 248, 263, 210, 233], [133, 298, 248, 263, 210, 66], [133, 298, 248, 263, 15], [133, 298, 248, 293, 66, 210], [133, 298, 248, 293, 247, 233, 210], [133, 298, 248, 293, 247, 15], [133, 298, 229, 233, 210, 263], [133, 298, 229, 233, 210, 293, 247], [133, 207, 229, 233, 263], [133, 207, 229, 233, 293, 247], [155, 70, 278, 168, 270, 252], [239, 105, 287, 170], [239, 229, 287, 170], [239, 284, 0, 238], [239, 284, 170, 238], [239, 117, 9, 285], [239, 117, 238, 285], [239, 117, 238, 170], [202, 183, 146, 216, 273, 277], [230, 158, 272, 250, 253, 259], [230, 158, 272, 250, 253, 118], [230, 158, 272, 250, 253, 166], [230, 158, 272, 80, 259], [230, 158, 272, 80, 118], [230, 158, 186, 80, 118], [46, 79, 257, 181], [146, 183, 192, 273, 277], [146, 183, 214, 216, 180], [146, 183, 214, 216, 273, 277], [170, 287, 203, 117], [170, 287, 203, 125], [170, 287, 203, 105], [170, 57, 105, 203], [157, 81, 215, 243, 167], [157, 215, 190, 243], [22, 97, 152, 87, 28, 1], [251, 100, 297, 211], [251, 100, 297, 32], [251, 178, 221, 297], [251, 178, 221, 112], [251, 297, 221, 32], [251, 297, 32, 257], [251, 181, 257, 32], [251, 181, 257, 79, 227], [251, 181, 32, 221], [251, 181, 227, 211], [238, 175, 285, 117], [238, 175, 285, 209, 284], [238, 175, 285, 136], [238, 175, 285, 8], [238, 175, 125, 8], [238, 175, 129, 136], [238, 175, 129, 8], [238, 30, 285, 117], [238, 0, 284, 209], [243, 81, 215, 246, 47, 167], [243, 81, 215, 246, 47, 188], [243, 215, 190, 246], [299, 131, 74, 141], [299, 131, 74, 124], [299, 131, 219, 124, 288], [2, 153, 29, 113, 173], [2, 153, 29, 113, 199], [246, 104, 130, 215, 81, 188], [246, 104, 130, 215, 190], [287, 209, 203, 121], [287, 209, 203, 105], [287, 209, 284, 59], [287, 121, 203, 125], [175, 203, 285, 117], [175, 203, 285, 209], [175, 59, 285, 284, 209], [175, 59, 285, 136], [195, 265, 221, 112, 98], [211, 100, 198, 191], [211, 100, 198, 297], [211, 181, 191, 227, 198], [280, 119, 24, 288], [292, 274, 268, 223, 196], [292, 274, 268, 271, 197, 196, 165], [292, 274, 268, 271, 197, 225], [292, 274, 268, 271, 197, 89], [292, 274, 268, 271, 34, 89], [292, 274, 41, 196, 223], [292, 274, 41, 196, 271], [292, 274, 41, 225, 271], [292, 274, 85, 197, 225], [292, 274, 85, 223], [292, 34, 142, 224], [292, 34, 268, 179, 89], [292, 34, 268, 224], [292, 165, 276, 271], [292, 165, 268, 164, 196, 271], [292, 165, 268, 164, 224], [292, 165, 268, 224, 197], [292, 197, 142, 85, 224], [292, 197, 142, 85, 225], [292, 196, 164, 64, 268], [292, 196, 223, 179, 147], [292, 196, 223, 179, 64, 268], [292, 196, 223, 179, 41], [292, 196, 271, 147], [292, 261, 276, 223, 179, 147], [292, 261, 276, 223, 179, 64], [292, 261, 276, 223, 179, 41], [292, 261, 276, 223, 85], [292, 261, 276, 142, 64], [292, 261, 276, 142, 225, 85], [292, 261, 276, 225, 41, 271], [292, 261, 276, 271, 147], [292, 261, 164, 142, 64, 224], [292, 261, 164, 142, 225], [292, 261, 164, 268, 64, 224], [292, 261, 164, 268, 271, 225], [292, 261, 164, 268, 271, 89], [292, 261, 268, 223, 64, 179], [292, 261, 268, 223, 64, 224], [292, 261, 268, 179, 89], [292, 261, 224, 223, 147], [292, 261, 224, 223, 41], [292, 261, 224, 223, 85], [292, 261, 224, 142, 85], [1, 87, 152, 43, 28, 21], [1, 87, 152, 43, 28, 97], [33, 86, 272, 118, 253, 250], [33, 86, 272, 118, 80], [209, 285, 121, 203], [166, 253, 86, 272, 231], [166, 253, 86, 272, 250], [288, 131, 95, 24, 124], [288, 131, 95, 24, 111], [288, 119, 283, 24, 95], [250, 86, 259, 253, 272], [57, 121, 203, 125], [57, 121, 203, 9], [247, 15, 248, 126, 298, 293], [247, 126, 298, 293, 210, 232, 248], [247, 126, 298, 293, 210, 232, 229], [247, 126, 298, 293, 210, 233, 248], [247, 126, 298, 293, 210, 233, 229], [285, 9, 203, 117], [285, 9, 203, 121], [171, 274, 85, 223], [171, 274, 85, 62, 225], [171, 62, 224, 85], [171, 165, 224, 240], [171, 261, 147, 223, 179], [171, 261, 147, 223, 224], [171, 261, 147, 240, 224], [171, 261, 225, 240], [171, 261, 225, 85], [171, 261, 85, 223, 224], [178, 265, 221, 297, 98], [178, 265, 221, 112, 98], [178, 265, 221, 112, 145], [297, 100, 198, 32], [297, 100, 198, 265], [297, 198, 32, 257], [66, 263, 236, 210, 232], [66, 263, 248, 298, 63], [66, 263, 248, 298, 210, 232], [32, 191, 198, 100], [32, 191, 198, 257, 181], [15, 248, 263, 298, 126], [15, 248, 38, 293], [265, 100, 191, 198], [181, 79, 198, 257, 227, 191], [173, 29, 6, 153], [63, 263, 229, 233, 298], [63, 263, 229, 233, 207], [80, 86, 272, 231, 259], [80, 86, 272, 231, 118], [232, 263, 248, 210, 298, 126], [232, 263, 229, 126, 210, 236], [232, 263, 229, 126, 210, 298], [232, 263, 229, 207, 236], [232, 126, 293, 236, 229, 210], [248, 263, 210, 233, 298, 126], [126, 229, 233, 210, 263, 236], [126, 229, 233, 210, 263, 298], [126, 229, 233, 210, 293, 236], [76, 50, 219, 131], [119, 283, 45, 95], [118, 86, 231, 253, 272], [272, 231, 86, 253, 259], [263, 236, 229, 233, 207], [62, 85, 225, 276], [240, 142, 224, 197], [240, 142, 224, 261], [240, 142, 225, 261, 276], [240, 224, 268, 197, 165], [240, 224, 268, 261], [240, 224, 41, 261], [240, 271, 41, 196, 274], [240, 271, 41, 225, 276, 261], [240, 271, 41, 225, 274], [240, 271, 268, 261, 225], [240, 271, 268, 274, 197, 196, 165], [240, 271, 268, 274, 197, 225], [240, 271, 261, 276, 147]]
+print(len(j))
+G = nx.read_edgelist(fh, nodetype=int)
+fh.close()
+M = np.empty((len(G),len(j)))
+print(M.shape)
+l = list(G.nodes)
+v = 0
+for i in l:
+    print(v)
+    v = v + 1
+    for line in range(len(j)):
+        #print(f[line])
+        if i in j[line]:
+            M[l.index(i), line] = 1
+        else:
+            M[l.index(i), line] = 0
+
+print(M)
+adjacency = nx.adjacency_matrix(G)
+#print(adjacency.shape)
+#print(adjacency)
+adjacency = adjacency.todense()
+#print(adjacency)
+adjacency = sparse.csr_matrix(adjacency)
+louvain = Louvain()
+#print(adjacency)
+
+#adjacency = karate_club()
+start = time.time()
+labels = louvain.fit_transform(adjacency)
+print("Time",time.time()-start)
+print(len(labels), np.asarray(labels))
+print("modularity Louvain",np.round(modularity(adjacency, labels), 10))
+
+M = sparse.csr_matrix(M)
+#print(M)
+start = time.time()
+labels2 = louvain.fit_transform(M)
+k = False
+#for i in range(len(labels)):
+#        print(i,labels[i],labels2[i])
+print(k)
+print("Time",time.time()-start)
+print(len(labels), labels2)
+
+print("modularity ta3na",np.round(modularity(adjacency, labels2), 10))
+
+
+p = []
+for i in range(len(labels)):
+    p.append(i)
+Y = []
+for i in range(len(set(labels))):
+    Y.append(i)
+print(Y)
+fo = open("example1.model", "w")
+strin = "{\"n_elements\": " + str(len(labels)) + ", \"n_clusters\": " + str(len(set(labels))) + ", \"elements\": " + str(p) + ", \"clusters\": " + str(Y)
+strin = strin + ", \"elm2clu_dict\": {"
+for i in range(len(labels)):
+    strin = strin + "\"" + str(i) + "\"" + ": [" + str(labels[i]) + "],"
+strin = strin[:-1] + "}, \"clu2elm_dict\": {"
+O = []
+for i in Y:
+    indices = []
+
+    for j in range(len(labels)):
+        if labels[j] == i:
+            indices.append(j)
+    O.append(len(indices))
+    strin = strin + "\"" + str(i) + "\": " + str(indices) +", "
+strin = strin[:-2] + "}" + ", \"hier_graph\": {\"directed\": true, \"multigraph\": false, \"graph\": {}, \"nodes\":[],\"links\": []}, \"clu_size_seq\": "+ str(O) +", \"is_disjoint\": true,\"is_hierarchical\": false, \"hierclusdict\": null}"
+fo.write(strin + "\n")
+fo.close()
+
+
+
+
+
+fo = open("example1.model", "a")
+strin = "{\"n_elements\": " + str(len(labels2)) + ", \"n_clusters\": " + str(len(set(labels2))) + ", \"elements\": " + str(p) + ", \"clusters\": " + str(Y)
+strin = strin + ", \"elm2clu_dict\": {"
+for i in range(len(labels2)):
+    strin = strin + "\"" + str(i) + "\"" + ": [" + str(labels2[i]) + "],"
+strin = strin[:-1] + "}, \"clu2elm_dict\": {"
+O = []
+for i in Y:
+    indices = []
+
+    for j in range(len(labels2)):
+        if labels2[j] == i:
+            indices.append(j)
+    O.append(len(indices))
+    strin = strin + "\"" + str(i) + "\": " + str(indices) +", "
+strin = strin[:-2] + "}" + ", \"hier_graph\": {\"directed\": true, \"multigraph\": false, \"graph\": {}, \"nodes\":[],\"links\": []}, \"clu_size_seq\": "+ str(O) +", \"is_disjoint\": true,\"is_hierarchical\": false, \"hierclusdict\": null}"
+fo.write(strin + "\n")
+fo.close()
+################ Propagation
+
+propagation = PropagationClustering()
+#graph = karate_club(metadata=True)
+#adjacency = graph.adjacency
+#print(adjacency)
+start = time.time()
+labels = propagation.fit_transform(M)
+print("Time",time.time()-start)
+print("PropagationClustering",len(set(labels)),labels)
+print("modularity ta3na",np.round(modularity(adjacency, labels), 10))
+start = time.time()
+labels = propagation.fit_transform(adjacency)
+print("Time",time.time()-start)
+print("PropagationClustering",len(set(labels)),labels)
+print("modularity propagation",np.round(modularity(adjacency, labels), 10))
+labels = propagation.fit_transform(M)
+print("modularity propagation ta3na",np.round(modularity(adjacency, labels), 10))
+
+
+
+
+
+
+fo = open("example1.model", "a")
+strin = "{\"n_elements\": " + str(len(labels)) + ", \"n_clusters\": " + str(len(set(labels))) + ", \"elements\": " + str(p) + ", \"clusters\": " + str(Y)
+strin = strin + ", \"elm2clu_dict\": {"
+for i in range(len(labels)):
+    strin = strin + "\"" + str(i) + "\"" + ": [" + str(labels[i]) + "],"
+strin = strin[:-1] + "}, \"clu2elm_dict\": {"
+O = []
+for i in Y:
+    indices = []
+
+    for j in range(len(labels)):
+        if labels[j] == i:
+            indices.append(j)
+    O.append(len(indices))
+    strin = strin + "\"" + str(i) + "\": " + str(indices) +", "
+strin = strin[:-2] + "}" + ", \"hier_graph\": {\"directed\": true, \"multigraph\": false, \"graph\": {}, \"nodes\":[],\"links\": []}, \"clu_size_seq\": "+ str(O) +", \"is_disjoint\": true,\"is_hierarchical\": false, \"hierclusdict\": null}"
+fo.write(strin + "\n")
+fo.close()
+
+
+from sklearn import datasets
+X = datasets.load_iris(return_X_y=False)
+print(X)
+from sklearn import metrics
+print(metrics.silhouette_score(adjacency, labels, metric='euclidean'))
+
+from sknetwork.data import house
+#adjacency = house()
+#labels = louvain.fit_transform(adjacency)
+#print("louvain",labels)
+#print(adjacency)
+#M = [[0,0,0,1],[0,0,0,1],[0,0,0,1],[1,1,1,0]]#,[0,0,0,0,0,1,1],[0,0,0,0,1,0,1],[0,0,0,0,1,1,0]]
+#M = sparse.csr_matrix(M)
+#labels = louvain.fit_transform(M)
+#print(M.shape,labels)
+nx.draw(G, with_labels=True, node_size=100)
+plt.show()
\ No newline at end of file
diff --git a/Train2.py b/Train2.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b9ae86716f3a283de39f84a13267230574d9027
--- /dev/null
+++ b/Train2.py
@@ -0,0 +1,97 @@
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import  Conv1D,MaxPool1D,Flatten,Dense,Activation,BatchNormalization,Dropout,LSTM#,CuDNNLSTM
+import numpy as np
+from keras.optimizers import SGD
+from keras import backend as K
+from keras.utils.generic_utils import get_custom_objects
+from tensorflow.keras.models import load_model
+import sklearn.preprocessing as s
+import time
+import matplotlib.pyplot as plt
+
+def step(x):
+    return K.relu(K.sigmoid(x))
+get_custom_objects().update({'step': Activation(step)})
+
+def load_data(DIR="subDataSet/EMB"):
+    train_x = np.load("data_train.npy",allow_pickle=True)
+    test_x = np.load("data_test.npy",allow_pickle=True)
+    val_x = np.load("data_val.npy",allow_pickle=True)
+    print(train_x[0].shape)
+    train_y = np.load("label_train.npy")
+    test_y = np.load("label_test.npy")
+    val_y= np.load("label_val.npy")
+    print(val_y.shape)
+
+    print(val_x[0])
+    print(val_y[0])
+    #normalize
+    #train_x = (train_x-train_x.min())/(train_x.max()-train_x.min())
+    #test_x = (test_x-test_x.min())/(test_x.max()-test_x.min())
+    #val_x = (val_x-val_x.min())/(val_x.max()-val_x.min())
+    return train_x,train_y,test_x,test_y,val_x,val_y
+
+def model(input_shape):
+    model = Sequential()
+
+    model.add(LSTM(1,return_sequences=True,input_shape=input_shape))
+    model.add(Flatten())
+    model.add(Dense(200))
+    model.add(Dense(100,activation="sigmoid"))
+    model.summary()
+    opt = tf.keras.optimizers.Adamax(learning_rate=0.01)
+    model.compile(loss='mse',optimizer="adamax",metrics=['binary_accuracy'])
+    return model
+
+train_x,train_y,test_x,test_y,val_x,val_y = load_data()
+model=model((100,6))
+print(train_x.shape)
+print(test_x.shape)
+print(val_x.shape)
+
+history = model.fit(train_x,train_y,validation_data=(val_x,val_y),epochs=300,batch_size=8)
+
+model.save('model_new.h5')
+
+model = load_model('model_new.h5')
+print(model.evaluate(test_x,test_y))
+
+print(history.history.keys())
+# summarize history for accuracy
+plt.plot(history.history['binary_accuracy'])
+plt.plot(history.history['val_binary_accuracy'])
+plt.title('model accuracy')
+plt.ylabel('accuracy')
+plt.xlabel('epoch')
+plt.legend(['train', 'test'], loc='upper left')
+plt.show()
+# summarize history for loss
+plt.plot(history.history['loss'])
+plt.plot(history.history['val_loss'])
+plt.title('model loss')
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(['train', 'test'], loc='upper left')
+plt.show()
+"""
+print( 2900 % 100)
+data_train=np.load('INoutput_data_val.npy')
+print("data_train",type(data_train),data_train.shape,data_train)
+tab = []
+start = time.time()
+for i in range(2900):
+    tab.append(data_train[i])
+    if len(tab) == 100:
+        tab = np.asarray(tab)
+        #print("data ",i,tab.shape)
+        tab = np.expand_dims(tab,axis=0)
+        pred = model.predict(tab)[0]
+        #print("pred", pred[24],tab[0,24])
+        pred = np.round(pred)
+        result = np.where(pred == 1)
+        print("pred 2", result[0]+(i-99),len(result[0]))
+        tab = []
+
+end = time.time()
+"""
diff --git a/Voisins.py b/Voisins.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c7c65d80332a715148d100f5afa9d035c05883e
--- /dev/null
+++ b/Voisins.py
@@ -0,0 +1,157 @@
+import networkx as nx
+import matplotlib.pyplot as plt
+from networkx.algorithms import clique
+
+def nodes_connected(u, v):
+    return u in G.neighbors(v)
+# debut de test
+#Generate Stars Graphs
+S = nx.star_graph(5)
+Z = nx.star_graph(10)
+
+fh = open("C:/Users/LENOVO/Desktop/karate.edgelist", "rb")
+G = nx.read_edgelist(fh, nodetype=int)
+
+#Generate Cliques Graphes
+cliques = list(clique.find_cliques(G))
+print("Cliques :",cliques)
+nx.draw(G,with_labels=True)  # networkx draw()
+plt.show()  # pyplot draw()
+
+X = []
+print(list(G[1]))
+#fin de test
+#fonction principale
+#Recuperation des voisins des hubs
+for i in range(len(G.nodes)):
+    y = list(G[i])
+    print(len(y),i)
+    if len(y) > 1:
+        X.append(y)
+        X.append(i)
+#Affichage
+print("Stars : ",X)
+#recuperation de la edgeliste (etape 0)
+#... à completer
+#creation du graphe à base de la edgelist donnée par le RNN (etape 2)
+l = nx.edges(G)
+P = nx.Graph(l)
+
+
+
+########## Bip ???!!!
+import networkx as nx
+from networkx.algorithms import community
+"""g = nx.Graph()
+g.add_edges_from([(1, 2), (3, 4), (3, 8), (1, 8)])"""
+blocks = community.kernighan_lin.kernighan_lin_bisection(G, partition=None, max_iter=10)
+print(blocks)
+nx.draw(G,with_labels=True)  # networkx draw()
+plt.show()  # pyplot draw()
+
+print(nx.is_bipartite(G))
+from networkx.algorithms import bipartite
+
+edges = G.edges()
+#X, Y = bipartite.sets(G)
+#print(list(X),list(Y))
+nx.draw(G,with_labels=True)  # networkx draw()
+plt.show()  # pyplot draw()
+
+##
+"""
+B = nx.Graph()
+# Add edges only between nodes of opposite node sets
+B.add_edges_from([(0, 4), (0, 5), (0,6), (1, 5), (1, 6), (1, 4), (2, 6), (3, 4), (7, 4), (7, 6), (7, 5),(8, 4), (8, 6), (8, 5),(0,9),(1,9),(9,7),(9,8)])
+"""
+X = []
+nx.is_connected(G)
+c = bipartite.color(G)
+print(c)
+bottom_nodes, top_nodes = bipartite.sets(G)
+print(bottom_nodes, top_nodes)
+#cliques = np.asarray(([y for x in cliques for y in x if len(x) >= 4]))
+
+"""
+for i in range(len(B.nodes)):
+    y = list(B[i])
+    print(len(y),i)
+    y.append(i)
+    if len(y) > 1:
+        X.append(y)
+Z = []
+for i in range (len(B.nodes)):
+    Y = []
+    for j in range(len(B.nodes)):
+        if i != j:
+            x = sorted(nx.common_neighbors(B, i, j))
+            if len(sorted(nx.common_neighbors(B, i, j))) > 0:
+                #x.append(i)
+                x.append(j)
+                #print("x",x)
+                Y.append(x)
+    Z.append(Y)
+    t = Z[i]
+    print("Y",i,Z[i])
+#print("X",X)
+check =  all(item in X[4] for item in X[3])
+
+if check:
+    print("true")
+else:
+    print("false",X[3],X[4])
+REZ = []
+for i in range(len(Z)):
+    t = Z[i]
+    for k in range(len(Z[i])):
+    #print(t)
+        set1 = set(t[k])
+        for j in range(len(Z)):
+            if (i != j):
+                v = Z[j]
+                for w in range(len(Z[j])):
+                        set2 = set(v[w])
+                        intersection = list(set1 & set2)
+                        intersection.append(i)
+                        intersection.append(j)
+                        print("REZ",intersection)
+                        REZ.append(intersection)
+nx.draw(B,with_labels=True)  # networkx draw()
+plt.show()
+"""
+r = list(bottom_nodes)
+print(r)
+for i in range(len(r)):
+    for j in range(len(r)):
+        #if r[i] != r[j] and nodes_connected(r[i],r[j]) == False:
+        G.add_edge(r[i], r[j])
+
+l = list(top_nodes)
+print(l)
+for i in range(len(l)):
+    for j in range(len(l)):
+        #if r[i] != r[j] and nodes_connected(r[i],r[j]) == False:
+        G.add_edge(l[i], l[j])
+
+print(G.edges)
+cliques = list(clique.enumerate_all_cliques(G))
+#cliques = np.asarray(([y for x in cliques for y in x if len(x) >= 4]))
+
+for i in range(len(cliques)):
+    #print(cliques[i])
+    cptb = 0
+    cptt = 0
+    if len(cliques[i]) >= 4: #taille minimale du bipartie
+        t = cliques[i]
+        for j in range(len(t)):
+            if r.count(t[j]) > 0:
+                cptb += 1
+            if l.count(t[j]) > 0:
+                cptt += 1
+        print(cptt,cptb)
+        if cptt >= 2 and cptb >= 2: #nombre minimale des elements dans chaque ensemble
+            print("bip",t)
+print("Cliques :",cliques)
+nx.draw(G,with_labels=True)  # networkx draw()
+plt.show()
+
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diff --git a/example1.model b/example1.model
new file mode 100644
index 0000000000000000000000000000000000000000..f2a378ae1afe0a5e32da0f55e73a88dde65fe43a
--- /dev/null
+++ b/example1.model
@@ -0,0 +1,3 @@
+{"n_elements": 300, "n_clusters": 12, "elements": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299], "clusters": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], "elm2clu_dict": {"0": [1],"1": [1],"2": [1],"3": [2],"4": [3],"5": [1],"6": [1],"7": [2],"8": [1],"9": [3],"10": [1],"11": [1],"12": [1],"13": [3],"14": [1],"15": [1],"16": [0],"17": [0],"18": [0],"19": [0],"20": [0],"21": [0],"22": [0],"23": [0],"24": [3],"25": [0],"26": [0],"27": [0],"28": [8],"29": [0],"30": [0],"31": [3],"32": [11],"33": [7],"34": [11],"35": [11],"36": [10],"37": [11],"38": [11],"39": [11],"40": [11],"41": [11],"42": [11],"43": [3],"44": [6],"45": [11],"46": [11],"47": 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"2": [33, 47, 89, 99, 145, 146, 147, 149, 151, 152, 153, 154, 188, 191, 192, 193, 194, 210, 240, 255, 256, 278, 280], "3": [32, 34, 35, 37, 38, 39, 40, 41, 42, 45, 46, 80, 81, 82, 111, 112], "4": [55, 69, 90, 120, 122, 123, 124, 127, 128, 129, 130, 131, 132, 134, 138, 140, 198, 200, 202, 203, 218, 271, 279, 283], "5": [67, 85, 119, 121, 139, 163, 166, 168, 170, 171, 173, 175, 176, 217, 219, 220, 221, 222, 224, 225, 284, 285], "6": [28, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 114, 117, 141, 143, 238, 239, 258, 259, 260, 262, 270], "7": [3, 7, 87, 94, 116, 144, 150, 172, 195, 196, 223, 230, 232, 233, 234, 235, 236, 237, 275, 276, 277, 281, 288, 289, 294, 295, 296, 297], "8": [51, 106, 161, 190, 248, 250, 251, 261, 263, 264, 265, 266, 267, 298], "9": [0, 1, 2, 5, 6, 8, 10, 11, 12, 14, 15, 88, 91, 92, 93, 95, 96, 100, 101, 103, 104, 107, 109, 113, 135, 187, 228, 268, 286, 287], "10": [36, 54, 65, 66, 68, 70, 71, 73, 74, 75, 76, 77, 79, 102, 105, 133, 137, 185, 186], "11": [182]}, "hier_graph": {"directed": true, "multigraph": false, "graph": {}, "nodes":[],"links": []}, "clu_size_seq": [21, 14, 23, 16, 24, 22, 23, 28, 14, 30, 19, 1], "is_disjoint": true,"is_hierarchical": false, "hierclusdict": null}
diff --git a/generation.py b/generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8a41187265ff5a5cfb42afab51d85ef857338dd
--- /dev/null
+++ b/generation.py
@@ -0,0 +1,67 @@
+from networkx.generators import community
+from networkx.generators import random_graphs
+from networkx.algorithms import clique
+import networkx as nx
+import random,numpy as np,os
+def generate_clique(nb,size,total_size):
+    sub=community.caveman_graph(nb,size)#Generation de de graphes en forme de cliques
+    G=random_graphs.fast_gnp_random_graph(total_size,0.05)#Generation de graphes aleatoires avec 0.1% de liens
+    G=nx.compose(G,sub) #fusion des deux graphes, obtention d'un graphe aleatoire avec nb cliques
+    node_mapping = dict(zip(G.nodes(), sorted(G.nodes(), key=lambda k: random.random())))#creation du mapping
+    G_new = nx.relabel_nodes(G, node_mapping)#application du mapping
+    cliques=list(clique.find_cliques(G_new))
+    cliques=np.asarray(([y for x in cliques for y in x  if len(x)>=4]))
+    nodes_cliques = np.unique(cliques)
+    print(len(nodes_cliques))
+    x = len(nodes_cliques)
+    output=np.zeros(total_size)
+    output[nodes_cliques]=1
+    return G_new,output,x,G_new.edges()
+
+def generate_without_clique(total_size):#generation de graphes aleatoires sans cliques
+    while True:
+        G=random_graphs.fast_gnp_random_graph(total_size,0.04)
+        cliques=list(clique.find_cliques(G))
+        cliques=[x for x in cliques if len(x)>=4]
+        if len(cliques)==0:
+            break
+    return G, np.zeros(total_size)
+
+
+def to_input_shape(G):# remplissage du fichier .edgelist format noeud -> noeud-voisin
+    tab=[]
+    for a,b in G.edges():
+        tab.append([a,b])
+    return tab
+
+BASE_PATH = "data"
+DIR ="clique_2"
+
+if(not os.path.exists(BASE_PATH)):
+    os.mkdir(BASE_PATH)
+PATH = os.path.join(BASE_PATH,DIR)
+
+if(not os.path.exists(PATH)):
+    os.mkdir(PATH)
+
+total_size = 100
+max_size_clique = 10
+max_clique_count = 10
+outputs = []
+Edgelist = []
+Gr_size = 1000
+data = []
+x = 0
+for id in range(Gr_size):
+    G,labels,y,edgelist = generate_clique(random.randint(4,max_clique_count),random.randint(4,max_size_clique),total_size)
+    x = x + y
+    outputs.append(labels)
+    tab = to_input_shape(G)
+    data.append(tab)
+    Edgelist.append(edgelist)
+np.save(os.path.join(PATH, "data.npy"), np.asarray(data))
+
+output = np.asarray(outputs)
+Edgelist = np.asarray(Edgelist)
+np.save(os.path.join(PATH,"labels.npy"),output) #generation des outputs
+np.save(os.path.join(PATH,"sam.npy"),Edgelist) #generation des outputs
\ No newline at end of file
diff --git a/graph_node_add.py b/graph_node_add.py
new file mode 100644
index 0000000000000000000000000000000000000000..d31d7688e080fa4787b8b198ed97e7ebfc447ce8
--- /dev/null
+++ b/graph_node_add.py
@@ -0,0 +1,16 @@
+from networkx.generators import community
+from networkx.generators import random_graphs
+from networkx.algorithms import clique
+import networkx as nx
+import random,numpy as np,os
+import copy
+import matplotlib.pyplot as plt
+
+G = random_graphs.fast_gnp_random_graph(10, 0.04)  # Generation de graphes aleatoires avec 0.1% de liens
+#nx.draw(G,with_labels=True)
+#plt.show()
+G.add_node(70)
+#nx.draw(G,with_labels=True)
+#plt.show()
+print(G.number_of_nodes())
+print(666 % 100)
\ No newline at end of file
diff --git a/merge.py b/merge.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e045152aa8227d17a21f4f57373a13079f1525b
--- /dev/null
+++ b/merge.py
@@ -0,0 +1,27 @@
+import os,numpy as np
+BASE_PATH = "data_val"
+#DIR ="clique_1"
+PATH = os.path.join(BASE_PATH)
+
+#merger les files dans un seule file "data.npy"
+def read(file):
+    file_path=os.path.join(PATH,file)
+    lines = open(file_path).read().splitlines()
+    output=[]
+    for line in lines:
+        output.append([int(x) for x in line.split(' ')])
+    output = np.asarray(output)
+    print(output)
+    return output
+
+
+data=[]
+for _,_,files in os.walk(os.path.join(PATH)):
+    for file in files:
+        if ".npy" not in file:
+            data.append(read(file))
+            print(data[-1].shape)
+data = np.asarray(data)
+np.save(os.path.join(PATH,"data.npy"),data)
+#data = np.load(os.path.join(PATH,"data.npy"),allow_pickle=True)
+print(data.shape)
diff --git a/merge_all.py b/merge_all.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1f2d90f439eb749286efd60f5488e49af77f623
--- /dev/null
+++ b/merge_all.py
@@ -0,0 +1,26 @@
+import os
+import numpy as np
+#merge les data.npy dans un seul npy
+BASE_PATH = "data"
+DIR ="clique"
+P = os.path.join(BASE_PATH, DIR)
+PATH = os.path.join(P, "data.npy")
+All_Data=list(np.load(PATH,allow_pickle=True))
+PATH = os.path.join(P, "labels.npy")
+ALL_Labels =list(np.load(PATH,allow_pickle=True))
+for i in range(2,7):
+    DIR="clique_"+str(i)
+    P = os.path.join(BASE_PATH,DIR)
+    PATH = os.path.join(P,"data.npy")
+    data = np.load(PATH,allow_pickle=True)
+    PATH = os.path.join(P, "labels.npy")
+    labels = np.load(PATH,allow_pickle=True)
+    All_Data.extend(list(data))
+    ALL_Labels.extend(list(labels))
+    print(data.shape)
+All_Data = np.asarray(All_Data)
+ALL_Labels = np.asarray(ALL_Labels)
+print(All_Data.shape)
+print(ALL_Labels.shape)
+np.save('data/data.npy',All_Data)
+np.save('data/labels.npy',ALL_Labels)
\ No newline at end of file
diff --git a/merge_embaded.py b/merge_embaded.py
new file mode 100644
index 0000000000000000000000000000000000000000..85b0a6830f0fd2700a4a656d944acaa0d91af229
--- /dev/null
+++ b/merge_embaded.py
@@ -0,0 +1,18 @@
+import os
+import numpy as np
+
+#combinaison des fichiers obtenus après l'embadding dans un seul tableau numpy pour chaque ensemble de données
+
+DIRS = ['data_test','data_val','data_train']
+for DIR in DIRS:
+    for _,_,files in os.walk(DIR):
+        data = np.zeros((len(files),100,6))
+        i = 0
+        for file in files:
+            t = np.load(os.path.join(DIR,file),allow_pickle=True)
+            print(t.shape,i)
+            data[i]=t
+            i+=1
+        np.save(os.path.join(DIR,DIR),data)
+        print(data.shape)
+    print(DIR,data)
\ No newline at end of file
diff --git a/model.h5 b/model.h5
new file mode 100644
index 0000000000000000000000000000000000000000..c35e10eccfd3eb1176a72f8862d2e17799daa88c
Binary files /dev/null and b/model.h5 differ
diff --git a/model2.h5 b/model2.h5
new file mode 100644
index 0000000000000000000000000000000000000000..c838a19ce908c2e6dd1a82aabd088815a003a8c7
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diff --git a/model_Bip.h5 b/model_Bip.h5
new file mode 100644
index 0000000000000000000000000000000000000000..183086842e92feab471e7bb9979c1e7049031a2a
Binary files /dev/null and b/model_Bip.h5 differ
diff --git a/model_new.h5 b/model_new.h5
new file mode 100644
index 0000000000000000000000000000000000000000..92073bfa077d2365e71269b41c7ece0b6d8da957
Binary files /dev/null and b/model_new.h5 differ
diff --git a/model_star.h5 b/model_star.h5
new file mode 100644
index 0000000000000000000000000000000000000000..d9d5faa5e8f317c5baf994ce7fd482263b250b71
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diff --git a/model_test.h5 b/model_test.h5
new file mode 100644
index 0000000000000000000000000000000000000000..cf054747543444b8e9c1bc145ce1863ea0e84cf4
Binary files /dev/null and b/model_test.h5 differ
diff --git a/model_train.h5 b/model_train.h5
new file mode 100644
index 0000000000000000000000000000000000000000..5fa200eafee008d678c89c681ef2dc183595ea50
Binary files /dev/null and b/model_train.h5 differ
diff --git a/split_data.py b/split_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..82c7580203019f9db99619d543ee8eaa068be612
--- /dev/null
+++ b/split_data.py
@@ -0,0 +1,83 @@
+import numpy as np
+
+#chargement des données
+#data  = np.load('data/clique_1/data.npy',allow_pickle=True)
+#matrix  = np.load('data/clique_1/data2.npy',allow_pickle=True)
+#out  = np.load('data/clique_1/output.npy',allow_pickle=True)
+size  = np.load('data/clique_1/size.npy',allow_pickle=True)
+labels = np.load('data/clique_1/labels2.npy',allow_pickle=True)
+#data = np.load('INoutput_data.npy')
+
+#print(data.shape)
+print(labels.shape)
+#print(matrix.shape)
+#print(out.shape)
+print(size.shape)
+
+#print(data[2])
+print(labels[0])
+#print(matrix[0])
+#print(out[0])
+print(size[0])
+
+print("DONE")
+
+#définition du ratio train / test
+split_rate=0.8
+k = int(len(size)*split_rate)
+
+#données d'apprentissage
+#data_train = data[:k]
+#matrix_train = matrix[:k]
+#out_train = out[:k]
+size_train = size[:k]
+label_train = labels[:k]
+
+#données de test
+#data_test = data[k:]
+#matrix_test = matrix[k:]
+size_test = size[k:]
+#out_test = out[k:]
+label_test = labels[k:]
+
+
+#définition du ratio train / validation
+split_rate=0.8
+k = int(len(size_train)*split_rate)
+
+#données de validation
+#data_val = data_train[k:]
+#matrix_val = matrix_train[k:]
+#out_val = out_train[k:]
+size_val = size_train[k:]
+label_val = label_train[k:]
+
+#donnée d'apprentissage
+#data_train = data_train[:k]
+#matrix_train = matrix_train[:k]
+#out_train = out_train[:k]
+size_train = size_train[:k]
+label_train = label_train[:k]
+
+print(label_train.shape,label_test.shape,label_val.shape)
+#print(data_train.shape,data_test.shape,data_val.shape)
+print(label_train[0])
+#print(data_train[0])
+#print(out_train.shape,out_test.shape,out_val.shape)
+print(size_train.shape,size_test.shape,size_val.shape)
+#sauvegarde
+#np.save("node2vec/src/graph/data_train.npy", data_train)
+#np.save("node2vec/src/graph/data_test.npy", data_test)
+#np.save("node2vec/src/graph/data_val.npy", data_val)
+#np.save("matrix_train.npy", matrix_train)
+#np.save("matrix_test.npy", matrix_test)
+#np.save("matrix_val.npy", matrix_val)
+#np.save("out_train.npy", out_train)
+#np.save("out_test.npy", out_test)
+#np.save("out_val.npy", out_val)
+np.save("node2vec/src/graph/size_train.npy", size_train)
+np.save("node2vec/src/graph/size_test.npy", size_test)
+np.save("node2vec/src/graph/size_val.npy", size_val)
+np.save("node2vec/src/graph/label_val.npy",label_val)
+np.save("node2vec/src/graph/label_train.npy",label_train)
+np.save("node2vec/src/graph/label_test.npy",label_test)
\ No newline at end of file
diff --git a/train.py b/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..62967ff762d5039b90a951d968b5911bff12acce
--- /dev/null
+++ b/train.py
@@ -0,0 +1,118 @@
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import  Conv1D,MaxPool1D,Flatten,Dense,Activation,BatchNormalization,Dropout,LSTM#,CuDNNLSTM
+import numpy as np
+from keras.optimizers import SGD
+from keras import backend as K
+from keras.utils.generic_utils import get_custom_objects
+from tensorflow.keras.models import load_model
+import sklearn.preprocessing as s
+import time
+
+def load_data(DIR="subDataSet/EMB"):
+    train_x = np.load("node2vec/src/graph/data_train.npy",allow_pickle=True)
+    test_x = np.load("node2vec/src/graph/data_test.npy",allow_pickle=True)
+    val_x = np.load("node2vec/src/graph/data_val.npy",allow_pickle=True)
+    print(train_x[0].shape)
+    train_y = np.load("node2vec/src/graph/label_train.npy")
+    test_y = np.load("node2vec/src/graph/label_test.npy")
+    val_y= np.load("node2vec/src/graph/label_val.npy")
+    print(val_x.shape)
+    print(val_y.shape)
+    print(train_x.shape)
+    print(train_y.shape)
+    print(test_x.shape)
+    print(test_y.shape)
+    print(val_y[0])
+    print("normalize",val_x[0])
+
+    return train_x,train_y,test_x,test_y,val_x,val_y
+
+def model(input_shape):
+    model = Sequential()
+    #model.add(Conv1D(80,3,input_shape=(1,100)))
+    #model.add(MaxPool1D(9))
+    #model.add(Conv1D(10,3))
+    #model.add(MaxPool1D(9))
+    #model.add(LSTM(1,return_sequences=True,input_shape=input_shape))
+    #model.add(LSTM(80))
+
+    #model.add(Flatten())
+    #model.add(Dropout(0.3))
+    model.add(Dense(200,input_shape=(100,)))
+    model.add(Dense(100,activation="sigmoid"))
+    model.summary()
+    opt = tf.keras.optimizers.Adamax(learning_rate=0.01)
+    model.compile(loss='mse',optimizer="adamax",metrics=['binary_accuracy'])
+    return model
+
+
+#train_x,train_y,test_x,test_y,val_x,val_y = load_data()
+#print("shape",train_x.shape)
+#train_x = train_x.reshape(-1,100,)
+#test_x  = test_x.reshape(-1,100,)
+#train_y = train_y.reshape(-1,100,)
+#test_y = test_y.reshape(-1,100,)
+#val_x  = val_x.reshape(-1,100,)
+#val_y = val_y.reshape(-1,100,)
+
+def Training():
+    #model = model((100,))
+
+    #model.fit(train_x,train_y,validation_data=(val_x,val_y),epochs=800,batch_size=8)
+    print("===================Start of Finding Patterns======================")
+    #model.save('Final_model.h5')
+    start = time.time()
+
+    model = load_model('Final_model.h5')
+    #print(model.evaluate(test_x, test_y))
+    #print(test_x.shape, test_x[0])
+    # data_train = np.load('INoutput_data_val.npy')
+    # data_tr = data_train.reshape(-1, 100)
+    # print(data_tr.shape)
+    #pred = model.predict(test_x)
+    #pred = np.round(pred)
+    #print(pred[0], len(pred), len(pred[0]))
+    # print( 900 % 100)
+    data_train = np.load('INoutput_data_val.npy')
+    print("data",data_train[1])
+    #data_train = np.append(data_train, -500)
+    #print(data_train, data_train.shape)
+    data_tr = data_train.reshape(-1, 100)
+    #print(data_tr[28])
+    #print(data_tr.shape)
+    pred = model.predict(data_tr)
+    pred = np.round(pred)
+    print("pred",pred)
+    #for i in range(len(pred)):
+    #    print(pred[i], np.count_nonzero(pred[i]))
+    # print("data_train", type(data_tr), data_train.shape, data_tr[0].shape)  # ,data_train,data_train[1])
+    """
+    tab = []
+    start = time.time()
+    for i in range(len(data_tr)):
+        # tab.append(data_tr[i])
+        # print(data_tr[i].shape)
+        pred = model.predict(data_tr[i])[0]
+        pred = np.round(pred)
+        if pred == 1:
+            print("pred : ", i, pred)
+        # print(len(tab))
+        
+        if len(tab) == 100:
+            tab = np.asarray(tab)
+            #print("data ",i,tab.shape)
+            tab = np.expand_dims(tab,axis=0)
+            pred = model.predict(tab)[0]
+            #print("pred", pred[24],tab[0,24])
+            pred = np.round(pred)
+            result = np.where(pred == 1)
+            print("pred 2", result[0]+(i-99),len(result[0]))
+            tab = []
+        """
+    end = time.time()
+    print("===================End of Finding Patterns======================")
+
+    print("Time", end - start)
+
+
diff --git a/transformation_clus.py b/transformation_clus.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ba93dcf10410e0bf6393fcadebe498cbe515a54
--- /dev/null
+++ b/transformation_clus.py
@@ -0,0 +1,119 @@
+import networkx as nx
+import matplotlib.pyplot as plt
+import numpy as np
+import copy
+from networkx.algorithms import clique
+import time
+f = open("C:/Users/LENOVO/Desktop/karate.edgelist", "r")
+
+"""
+x = [[2,7,10,19,30,42,47,53,61],[1,5,6,9,13,17,22,25,26,27,31,32,41,48,54,56,57,60],[3,8,28,36,39,59],[12,14,16,20,33,34,37,38,40,43,44,46,49,50,52,58]]
+for i in x:
+    for j in i:
+        for k in i:
+            if j < k:
+                print("x[",j,",",k,"] <- NA")
+
+f = open("C:/Users/LENOVO/Desktop/karate.edgelist", "r")
+G = nx.read_edgelist(f,nodetype=int)
+x = []
+Y = []
+for i in range(len(G.nodes)):
+    x.append(list(G.neighbors(i)))
+    Y.append(i)
+    #print(i,list(G.neighbors(i)))
+print(x)
+c = []
+for i in range(len(x)):
+    c = []
+    for j in x[i]:
+        if j > i:
+            c.append(j)
+    x[i] = c
+print(x)
+for i in range(len(x)):
+    for j in range(len(x)):
+        v = []
+        t = 0
+        if len(x[i]) > len(x[j]):
+            #print(x[i],x[j])
+            v.append(x[i])
+            t = Y[i]
+            x[i] = x[j]
+            x[j] = v[0]
+            Y[i] = Y [j]
+            Y[j] = t
+            #print(x[i], x[j])
+print(x)
+print(Y,len(Y))
+for i in x:
+    for j in range(len(i)):
+        i[j] = i[j] + 1
+print(x)
+
+R = np.zeros(len(Y))
+for i in range(len(R)):
+    for j in x:
+        if i + 1 in j:
+            R[i] = R[i] + 1
+print(R,len(R))
+print(len(G.edges))
+P =[]
+for i in range(len(Y)):
+    for j in x:
+        if i+1 in j:
+            j[j.index(i+1)] = Y[i]
+            print("index ",i+1,j)
+for i in range(len(Y)):
+    for j in x[i]:
+        print("x[",i+1,",",j,"] <- NA")
+"""
+
+f1 = open("C:/Users/LENOVO/Desktop/o.txt","r")
+
+paragraph = f1.readlines()
+print("hna")
+r = []
+x = []
+for line in paragraph:
+    #f1.write(line.strip()+",1\n")
+    tmp = line.strip().split(' ')
+    x.append(tmp[1])
+    if int(tmp[1]) not in r:
+        r.append(int(tmp[1]))
+    #print(tmp[0])
+    #print(tmp[1])
+    #x = []
+    #x.append(int(tmp[0]))
+    #x.append(int(tmp[1]))
+    #if tmp[0] != tmp[1] and sorted(x) not in r:
+    #r.append(sorted(x))
+print(r,len(x))
+# f1.write(str(int(tmp[0]))+","+str(int(tmp[1]))+"\n")
+p = []
+for i in range(len(x)):
+    p.append(i)
+Y = []
+for i in range(len(set(x))):
+    Y.append(i)
+print(Y)
+fo = open("example1.model", "w")
+strin = "{\"n_elements\": " + str(len(x)) + ", \"n_clusters\": " + str(len(set(x))) + ", \"elements\": " + str(p) + ", \"clusters\": " + str(Y)
+strin = strin + ", \"elm2clu_dict\": {"
+for i in range(len(x)):
+    strin = strin + "\"" + str(i) + "\"" + ": [" + str(x[i]) + "],"
+strin = strin[:-1] + "}, \"clu2elm_dict\": {"
+O = []
+print("fffff",len(x))
+for i in Y:
+    indices = []
+
+    for j in range(len(x)):
+        if int(x[j]) == i+1:
+            indices.append(j)
+    O.append(len(indices))
+    print("oooooooooo",O)
+    strin = strin + "\"" + str(i+1) + "\": " + str(indices) +","
+strin = strin[:-2] + "}" + ", \"hier_graph\": {\"directed\": true, \"multigraph\": false, \"graph\": {}, \"nodes\":[],\"links\": []}, \"clu_size_seq\": "+ str(O) +", \"is_disjoint\": true,\"is_hierarchical\": false, \"hierclusdict\": null}"
+fo.write(strin + "\n")
+fo.close()
diff --git a/transformed_0.npy b/transformed_0.npy
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