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/README.md b/README.md
index c0ba099c01c813a81db202cf9b5fe8f16617ee41..1d72efbfd6d1bf8618af8586313ca134e54d3bee 100644
--- a/README.md
+++ b/README.md
@@ -44,22 +44,15 @@ Use the built-in continuous integration in GitLab.
 
 ***
 
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!).  Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
 ## Name
-**DeepDense : Enabling Node Embedding to DenseSubgraph Mining**
+**DeepDense: Enabling Node Embedding to DenseSubgraph Mining**
 ## Description
 Dense subgraphs convey important information and insights about a graph structure. This explains why dense subgraph mining is a  problem of key interest that arises in several tasks and applications such as graph visualization, graph summarization, graph clustering, and complex network analysis. It is a hard problem that has been intensively addressed in the data mining community.
 In this paper, we propose a deep learning approach that enumerates almost all occurrences of dense subgraphs in a graph without any constraints or limitations on their size. More precisely, we enrich exiting structural node embedding with extra information computed on node neighborhoods to capture their belonging to specific types of dense subgraphs, leading to more meaningful embedding. We evaluate our approach on several datasets to attest its efficiency on two main applications, namely, graph summarization and graph clustering.
-![ScreenShot](.jpg) 
+
 ## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
 
+![ScreenShot](/Sub-graphs-discovernigg.png)
 ## Installation
 Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
 
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, 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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/Sub-graphs-discovernigg.png b/Sub-graphs-discovernigg.png
new file mode 100644
index 0000000000000000000000000000000000000000..532dc9eeb6fbf80d4f27e320468f5f7a277536ff
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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, 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[9],"229": [14],"230": [7],"231": [12],"232": [7],"233": [7],"234": [7],"235": [7],"236": [7],"237": [7],"238": [6],"239": [6],"240": [2],"241": [14],"242": [14],"243": [14],"244": [14],"245": [13],"246": [13],"247": [13],"248": [8],"249": [13],"250": [8],"251": [8],"252": [12],"253": [14],"254": [12],"255": [2],"256": [2],"257": [15],"258": [6],"259": [6],"260": [6],"261": [8],"262": [6],"263": [8],"264": [8],"265": [8],"266": [8],"267": [8],"268": [9],"269": [14],"270": [6],"271": [4],"272": [0],"273": [0],"274": [12],"275": [7],"276": [7],"277": [7],"278": [2],"279": [4],"280": [2],"281": [7],"282": [14],"283": [4],"284": [5],"285": [5],"286": [9],"287": [9],"288": [7],"289": [7],"290": [15],"291": [14],"292": [14],"293": [13],"294": [7],"295": [7],"296": [7],"297": [7],"298": [8],"299": [13]}, "clu2elm_dict": {"0": [29, 30, 148, 158, 167, 174, 177, 178, 179, 180, 181, 183, 184, 199, 212, 213, 214, 215, 216, 272, 273], "1": [16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 48, 49, 50], "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
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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
new file mode 100644
index 0000000000000000000000000000000000000000..ba8651317378af0f526bd93a61b9dc3fd07a3221
Binary files /dev/null and b/transformed_0.npy differ