diff --git a/results_LFR_graphs.py b/results_LFR_graphs.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe18799de98da4ca7a33247a649dace88f44b260
--- /dev/null
+++ b/results_LFR_graphs.py
@@ -0,0 +1,236 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+def lire_fichier_donnees(filename):
+ file = open(filename, 'r')
+
+ file.readline().rstrip('\n\r') #on oublie les 2 premieres lignes
+ file.readline().rstrip('\n\r')
+ file.readline().rstrip('\n\r')
+ file.readline().rstrip('\n\r')
+
+ nb_ligne = 0
+
+ ligne = file.readline().rstrip('\n\r')
+ file.readline().rstrip('\n\r')
+
+ t_baseline_true = 0
+ comp_baseline_true = 0
+ t_red_aware_true = 0
+ comp_red_aware_true = 0
+ t_del_edge_true = 0
+ comp_del_edge_true = 0
+ t_greedy_true = 0
+ comp_greedy_true = 0
+ t_baseline_false = 0
+ comp_baseline_false = 0
+ t_red_aware_false = 0
+ comp_red_aware_false = 0
+ t_del_edge_false = 0
+ comp_del_edge_false = 0
+ t_greedy_false = 0
+ comp_greedy_false = 0
+
+ print(filename)
+
+ while ligne:
+ nb_ligne += 1
+ l = ligne.split()
+
+ print("ligne:",ligne)
+
+ t_baseline_true += float(l[0])
+ comp_baseline_true += float(l[1])
+ t_baseline_false += float(l[2])
+ comp_baseline_false += float(l[3])
+ t_del_edge_true += float(l[4])
+ comp_del_edge_true += float(l[5])
+ t_del_edge_false += float(l[6])
+ comp_del_edge_false += float(l[7])
+ t_red_aware_true += float(l[8])
+ comp_red_aware_true += float(l[9])
+ t_red_aware_false += float(l[10])
+ comp_red_aware_false += float(l[11])
+ t_greedy_true += float(l[12])
+ comp_greedy_true += float(l[13])
+ t_greedy_false += float(l[14])
+ comp_greedy_false += float(l[15])
+
+
+ ligne = file.readline().rstrip('\n\r')
+ file.readline().rstrip('\n\r')
+
+ file.close()
+
+ return np.array([t_baseline_true, comp_baseline_true, t_baseline_false, comp_baseline_false, t_del_edge_true, comp_del_edge_true, t_del_edge_false, comp_del_edge_false, t_red_aware_true, comp_red_aware_true, t_red_aware_false, comp_red_aware_false, t_greedy_true, comp_greedy_true, t_greedy_false, comp_greedy_false])/nb_ligne
+
+
+
+t_baseline_true = []
+comp_baseline_true = []
+t_baseline_false = []
+comp_baseline_false = []
+t_del_edge_true = []
+comp_del_edge_true = []
+t_del_edge_false = []
+comp_del_edge_false = []
+t_red_aware_true = []
+comp_red_aware_true = []
+t_red_aware_false = []
+comp_red_aware_false = []
+t_greedy_true = []
+comp_greedy_true = []
+t_greedy_false = []
+comp_greedy_false = []
+
+liste_mus = [20,22,24,26,28,30,32,34,37,39,41,43,45,47,49,51,53,55,56,59,61,63,66,68,70,72,74,76,78,80]
+
+for mu in liste_mus:
+ l = lire_fichier_donnees("results\\LFR_graphs\\LFR_graphs_300n_"+str(mu)+"mu.txt")
+ t_baseline_true.append(l[0])
+ comp_baseline_true.append(l[1])
+ t_baseline_false.append(l[2])
+ comp_baseline_false.append(l[3])
+ t_del_edge_true.append(l[4])
+ comp_del_edge_true.append(l[5])
+ t_del_edge_false.append(l[6])
+ comp_del_edge_false.append(l[7])
+ t_red_aware_true.append(l[8])
+ comp_red_aware_true.append(l[9])
+ t_red_aware_false.append(l[10])
+ comp_red_aware_false.append(l[11])
+ t_greedy_true.append(l[12])
+ comp_greedy_true.append(l[13])
+ t_greedy_false.append(l[14])
+ comp_greedy_false.append(l[15])
+
+#t_del_edge_true = [0.13769824504852296, 0.015675163269042967, 0.015669846534729005, 0.012603402137756348, 0.010821032524108886, 0.00785224437713623, 0.012531638145446777, 0.010931086540222169, 0.006303906440734863, 0.007808184623718262, 0.01097433567047119, 0.012548470497131347, 0.007838010787963867, 0.009438872337341309, 0.009387493133544922, 0.012825989723205566, 0.012174725532531738, 0.012514567375183106, 0.012510108947753906, 0.010909557342529297, 0.012520813941955566, 0.012698698043823241, 0.009384441375732421, 0.009386157989501953, 0.009468150138854981, 0.015651726722717287, 0.01255035400390625, 0.014095330238342285, 0.012532925605773926, 0.011045503616333007]
+#t_del_edge_false = [3.873183012008667, 3.459059715270996, 3.185631442070007, 3.251274299621582, 3.2998734951019286, 3.2951504230499267, 3.442635989189148, 3.836528277397156, 3.744323754310608, 3.8696754217147826, 3.881132459640503, 4.0867551326751705, 4.237580943107605, 4.283705806732177, 4.579205393791199, 4.7296847105026245, 4.914470314979553, 5.085553932189941, 5.163970041275024, 5.063702344894409, 5.327744674682617, 5.3884907245635985, 5.404787492752075, 5.554291439056397, 5.639377927780151, 5.728605818748474, 5.7852600574493405, 5.65103280544281, 5.827508163452149, 5.586722898483276]
+
+"""
+comp_baseline_true = np.array(comp_baseline_true)/2 + 500
+comp_baseline_false = np.array(comp_baseline_false)/2 + 500
+comp_red_aware_true = np.array(comp_red_aware_true)-1000
+comp_red_aware_false = np.array(comp_red_aware_false)-1000
+comp_greedy_true = np.array(comp_greedy_true)+1000
+comp_greedy_false = np.array(comp_greedy_false)+1000"""
+
+x = 0.01*np.array(liste_mus)
+
+text_size = 15
+avec_legend = False
+
+plt.figure()
+plt.title("Runtime with pre-processing (in s)",fontsize=text_size)
+plt.plot(x,t_baseline_true,color="red",label="Quick")
+plt.plot(x,t_red_aware_true,color="blue",label="Quick_redundancy_aware")
+plt.plot(x,t_del_edge_true,color="green",label="Quick_delete_covered_edges")
+plt.plot(x,t_greedy_true,color="orange",label="Greedy_quasi_cliques")
+#plt.xlabel(chr(946),fontsize=30)
+#plt.ylabel("runtime",fontsize=30)
+if avec_legend:
+ plt.legend()
+plt.savefig("figures\\LFR_graphs\\all_with_time.png",format='png')
+
+plt.figure()
+plt.title("Runtime without pre-processing (in s)",fontsize=text_size)
+plt.plot(x,t_baseline_false,color="red",label="quick")
+plt.plot(x,t_red_aware_false,color="blue",label="red_aware")
+plt.plot(x,t_del_edge_false,color="green",label="del_edges")
+plt.plot(x,t_greedy_false,color="orange",label="greedy")
+if avec_legend:
+ plt.legend()
+plt.savefig("figures\\LFR_graphs\\all_without_time.png",format='png')
+
+
+plt.figure()
+plt.title("Quick runtime (in s)",fontsize=text_size)
+plt.plot(x,t_baseline_true,color="red",label="with pre-processing")
+plt.plot(x,t_baseline_false,color="blue",label="without pre-processing")
+if avec_legend:
+ plt.legend()
+plt.savefig("figures\\LFR_graphs\\quick_time.png",format='png')
+
+plt.figure()
+plt.title("Quick_redundancy_aware runtime (in s)",fontsize=text_size)
+plt.plot(x,t_red_aware_true,color="red",label="with pre-processing")
+plt.plot(x,t_red_aware_false,color="blue",label="without pre-processing")
+if avec_legend:
+ plt.legend()
+plt.savefig("figures\\LFR_graphs\\red_aware_time.png",format='png')
+
+plt.figure()
+plt.title("Quick_delete_covered_edges (in s)",fontsize=text_size)
+plt.plot(x,t_del_edge_true,color="red",label="with pre-processing")
+plt.plot(x,t_del_edge_false,color="blue",label="without pre-processing")
+if avec_legend:
+ plt.legend()
+plt.savefig("figures\\LFR_graphs\\del_edges_time.png",format='png')
+
+plt.figure()
+plt.title("Greedy_quasi_cliques runtime (in s)",fontsize=text_size)
+plt.plot(x,t_greedy_true,color="red",label="with pre-processing")
+plt.plot(x,t_greedy_false,color="blue",label="without pre-processing")
+if avec_legend:
+ plt.legend()
+plt.savefig("figures\\LFR_graphs\\greedy_time.png",format='png')
+
+
+plt.figure()
+plt.title("Summary size with pre-processing",fontsize=text_size)
+plt.plot(x,comp_baseline_true,color="red",label="quick")
+plt.plot(x,comp_red_aware_true,color="blue",label="red_aware")
+plt.plot(x,comp_del_edge_true,color="green",label="del_edges")
+plt.plot(x,comp_greedy_true,color="orange",label="greedy")
+if avec_legend:
+ plt.legend()
+plt.savefig("figures\\LFR_graphs\\all_with_size.png",format='png')
+
+plt.figure()
+plt.title("Summary size without pre-processing",fontsize=text_size)
+plt.plot(x,comp_baseline_false,color="red",label="Quick")
+plt.plot(x,comp_red_aware_false,color="blue",label="Redundancy aware")
+plt.plot(x,comp_del_edge_false,color="green",label="Delete covered edges")
+plt.plot(x,comp_greedy_false,color="orange",label="Greedy")
+if avec_legend:
+ plt.legend()
+plt.savefig("figures\\LFR_graphs\\all_without_size.png",format='png')
+
+
+plt.figure()
+plt.title("Summary size with Quick",fontsize=text_size)
+plt.plot(x,comp_baseline_true,color="red",label="with pre-processing")
+plt.plot(x,comp_baseline_false,color="blue",label="without pre-processing")
+if avec_legend:
+ plt.legend()
+plt.savefig("figures\\LFR_graphs\\quick_size.png",format='png')
+
+plt.figure()
+plt.title("Summary size with Quick_redundancy_aware",fontsize=text_size)
+plt.plot(x,comp_red_aware_true,color="red",label="with pre-processing")
+plt.plot(x,comp_red_aware_false,color="blue",label="without pre-processing")
+if avec_legend:
+ plt.legend()
+plt.savefig("figures\\LFR_graphs\\red_aware_size.png",format='png')
+
+plt.figure()
+plt.title("Summary size with Quick_delete_covered_edges",fontsize=text_size)
+plt.plot(x,comp_del_edge_true,color="red",label="with pre-processing")
+plt.plot(x,comp_del_edge_false,color="blue",label="without pre-processing")
+if avec_legend:
+ plt.legend()
+plt.savefig("figures\\LFR_graphs\\del_edges_size.png",format='png')
+
+plt.figure()
+plt.title("Summary size with Greedy_quasi_cliques",fontsize=text_size)
+plt.plot(x,comp_greedy_true,color="red",label="with pre-processing")
+plt.plot(x,comp_greedy_false,color="blue",label="without pre-processing")
+if avec_legend:
+ plt.legend()
+plt.savefig("figures\\LFR_graphs\\greedy_size.png",format='png')
+
+
+
+
+