├── 2-karate.gephi ├── README.md ├── algorithm ├── GN-weighted.py ├── Girvan-Newman.py ├── outputofGN.gml └── outputofGN_weighted.gml ├── datasets ├── karate.gml └── lesmiserables.gml ├── maxQkarate.gephi └── maxQlesmiserables.gephi /2-karate.gephi: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sikasjc/CommunityDetection/7d9a4058380c02a6200b4a5dad7fab33b7dfe975/2-karate.gephi -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [中文][1] 2 | 3 | [English][2] 4 | 5 | ## 介绍 6 | 关于**社区发现**和**Girvan_Newman算法**： 7 | 8 | - 社区发现：[Wiki][3] 9 | - 我的博客：[GN算法][4]， [针对加权图的GN算法][5] 10 | - Girvan–Newman算法：[Wiki][6] 11 | 12 | 13 | ## 实现： 14 | 15 | ### Girvan_Newman算法 16 | 17 | **Zachary's karate club网络：** 18 | ![Zachary's karate club][7] 19 | 20 | **划分后的网络：** 21 | ![Zachary's karate club][8] 22 | 23 | ### 针对加权网络的Girvan_Newman算法 24 | 25 | **Les Miserables网络：** 26 | ![Les miserables][9] 27 | 28 | **划分后的网络：** 29 | ![Les miserables][10] 30 | 31 | > 建议使用 [Gephi](https://gephi.org/ "Gephi") 显示数据集文件，可以为网络提供一个非常直观的展示 32 | 33 | ## 数据集 34 | **Zachary's karate club**: social network of friendships between 34 members of a karate club at a US university in the 1970s. W. W. Zachary, An information flow model for conflict and fission in small groups, Journal of Anthropological Research 33, 452-473 (1977). 35 | 36 | **Les Miserables**: coappearance weighted network of characters in the novel Les Miserables. D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993). 37 | 38 | 这两个数据集均来自Gephi 的 [Sample Datasets](https://github.com/gephi/gephi/wiki/Datasets "Datasets") 39 | 40 | Gephi sample datasets 提供了有非常丰富的关于网络的数据集 41 | 42 | ## 参考文献 43 | 44 | Newman M E J, Girvan M. Finding and evaluating community structure in networks[J]. Physical review E, 2004, 69(2): 026113. 45 | 46 | ------------ 47 | 48 | ## Introduction 49 | Some about **Community Structure**和**Girvan–Newman Algorithm**： 50 | 51 | - Community Structure：[Wiki][11] 52 | - My post(Chinese) ：[GN算法][12]， [针对加权图的GN算法][13] 53 | - Girvan–Newman algorithm：[Wiki][14] 54 | 55 | ## Implementation： 56 | 57 | ### Girvan_Newman Algorithm 58 | 59 | **Original Network：** 60 | ![Zachary's karate club][15] 61 | 62 | **Divided network：** 63 | ![Zachary's karate club][16] 64 | 65 | ### Girvan_Newman Algorithm for weighted graph 66 | 67 | **Original Network：** 68 | ![Lesmiserables][17] 69 | 70 | **Divided network：** 71 | ![Lesmiserables][18] 72 | 73 | > It is recommended to use [Gephi](https://gephi.org/ "Gephi") to display dataset files. And it can provide a very intuitive presentation of networks 74 | 75 | ## Datasets 76 | 77 | ### The Girvan_Newman algorithm uses the Zachary's karate club dataset 78 | **Zachary's karate club**: social network of friendships between 34 members of a karate club at a US university in the 1970s. W. W. Zachary, An information flow model for conflict and fission in small groups, Journal of Anthropological Research 33, 452-473 (1977). 79 | 80 | ### The weighted Girvan_Newman algorithm uses the Les Miserables dataset 81 | **Les Miserables**: coappearance weighted network of characters in the novel Les Miserables. D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993). 82 | 83 | Both datasets are from [Gephi sample datasets](https://github.com/gephi/gephi/wiki/Datasets "Datasets") 84 | 85 | ## References 86 | Newman M E J, Girvan M. Finding and evaluating community structure in networks[J]. Physical review E, 2004, 69(2): 026113. 87 | 88 | 89 | [1]: https://github.com/sikasjc/CommunityDetection#%E4%BB%8B%E7%BB%8D 90 | [2]: https://github.com/sikasjc/CommunityDetection#introduction 91 | [3]: https://en.wikipedia.org/wiki/Community_structure 92 | [4]: https://sikasjc.coding.me/2017/12/20/GN/ 93 | [5]: https://sikasjc.github.io/2018/04/28/weighted_GN/ 94 | [6]: https://en.wikipedia.org/wiki/Girvan%E2%80%93Newman_algorithm 95 | [7]: https://i.postimg.cc/X7GXdNws/79561109.png 96 | [8]: https://i.postimg.cc/x8q8snBT/17264809.png 97 | [9]: https://i.postimg.cc/fRv3T27s/75989614.png 98 | [10]: https://i.postimg.cc/m2CzK2tt/8315054.png 99 | [11]: https://en.wikipedia.org/wiki/Community_structure 100 | [12]: https://sikasjc.coding.me/2017/12/20/GN/ 101 | [13]: https://sikasjc.github.io/2018/04/28/weighted_GN/ 102 | [14]: https://en.wikipedia.org/wiki/Girvan%E2%80%93Newman_algorithm 103 | [15]: https://i.postimg.cc/X7GXdNws/79561109.png 104 | [16]: https://i.postimg.cc/x8q8snBT/17264809.png 105 | [17]: https://i.postimg.cc/fRv3T27s/75989614.png 106 | [18]: https://i.postimg.cc/m2CzK2tt/8315054.png 107 | -------------------------------------------------------------------------------- /algorithm/GN-weighted.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import networkx as nx 4 | import matplotlib.pyplot as plt 5 | import sys 6 | sys.path.append('../') 7 | 8 | class GN_w: 9 | def __init__(self, G): 10 | self.G_copy = G.copy() 11 | self.G = G 12 | self.partition = [[n for n in G.nodes()]] 13 | self.all_Q = [0.0] 14 | self.max_Q = 0.0 15 | 16 | #Using max_Q to divide communities 17 | def run(self): 18 | # #Until there is no edge in the graph 19 | # while len(self.G.edges()) != 0: 20 | # #Find the most betweenness edge 21 | # edge = max(nx.edge_betweenness_centrality(self.G).items(),key=lambda item:item[1])[0] 22 | # #Remove the most betweenness edge 23 | # self.G.remove_edge(edge[0], edge[1]) 24 | # #List the the connected nodes 25 | # components = [list(c) for c in list(nx.connected_components(self.G))] 26 | # if len(components) != len(self.partition): 27 | # #compute the Q 28 | # cur_Q = self.cal_Q(components, self.G_copy) 29 | # if cur_Q not in self.all_Q: 30 | # self.all_Q.append(cur_Q) 31 | # if cur_Q > self.max_Q: 32 | # self.max_Q = cur_Q 33 | # self.partition = components 34 | # 35 | # print('-----------the Max Q and divided communities-----------') 36 | # print('The number of Communites:', len(self.partition)) 37 | # print("Communites:", self.partition) 38 | # print('Max_Q:', self.max_Q) 39 | # return self.partition, self.all_Q, self.max_Q 40 | 41 | while len(self.G.edges()) != 0: 42 | edges={} 43 | edges_betweenness_centrality = nx.edge_betweenness_centrality(self.G) 44 | 45 | for e, ebc in edges_betweenness_centrality.items(): 46 | edge_weight = ebc/self.G.get_edge_data(e[0],e[1])['value'] 47 | edges[e]=edge_weight 48 | 49 | edge = max(edges.items(), key=lambda item:item[1])[0] 50 | self.G.remove_edge(edge[0], edge[1]) 51 | components = [list(c) for c in list(nx.connected_components(self.G))] 52 | if len(components) != len(self.partition): 53 | #compute the Q 54 | cur_Q = self.cal_Q(components, self.G_copy) 55 | if cur_Q not in self.all_Q: 56 | self.all_Q.append(cur_Q) 57 | if cur_Q > self.max_Q: 58 | self.max_Q = cur_Q 59 | self.partition = components 60 | 61 | print('-----------the Max Q and divided communities-----------') 62 | print('The number of Communites:', len(self.partition)) 63 | print("Communites:", self.partition) 64 | print('Max_Q:', self.max_Q) 65 | return self.partition, self.all_Q, self.max_Q 66 | 67 | 68 | #the process of divding the network 69 | #Return a list containing the result of each division, until each node is a community 70 | def run_to_all(self): 71 | divide = [] 72 | all_Q = [] 73 | while len(self.G.edges()) != 0: 74 | edge = max(nx.edge_betweenness_centrality(self.G).items(),key=lambda item:item[1])[0] 75 | self.G.remove_edge(edge[0], edge[1]) 76 | components = [list(c) for c in list(nx.connected_components(self.G))] 77 | if components not in divide: 78 | divide.append(components) 79 | cur_Q = self.cal_Q(components, self.G_copy) 80 | if cur_Q not in all_Q: 81 | all_Q.append(cur_Q) 82 | return divide, all_Q 83 | 84 | #Drawing the graph of Q and divided communities 85 | def draw_Q(self): 86 | plt.plot([x for x in range(1,len(self.G.nodes)+1)],self.all_Q) 87 | my_x_ticks = [x for x in range(1,len(self.G.nodes)+1)] 88 | plt.xticks(my_x_ticks) 89 | plt.axvline(len(self.partition),color='black',linestyle="--") 90 | #plt.axhline(self.all_Q[3],color='red',linestyle="--") 91 | plt.show() 92 | 93 | def add_group(self): 94 | num = 0 95 | nodegroup = {} 96 | for partition in self.partition: 97 | for node in partition: 98 | nodegroup[node] = {'group':num} 99 | num = num + 1 100 | nx.set_node_attributes(self.G_copy, nodegroup) 101 | 102 | def to_gml(self): 103 | nx.write_gml(self.G_copy, 'outputofGN_weighted.gml') 104 | 105 | #Computing the Q 106 | def cal_Q(self,partition,G): 107 | m = len(G.edges(None, False)) 108 | a = [] 109 | e = [] 110 | 111 | for community in partition: 112 | t = 0.0 113 | for node in community: 114 | t += len([x for x in G.neighbors(node)]) 115 | a.append(t/(2*m)) 116 | 117 | for community in partition: 118 | t = 0.0 119 | for i in range(len(community)): 120 | for j in range(len(community)): 121 | if(G.has_edge(community[i], community[j])): 122 | t += 1.0 123 | e.append(t/(2*m)) 124 | 125 | q = 0.0 126 | for ei,ai in zip(e,a): 127 | q += (ei - ai**2) 128 | return q 129 | 130 | if __name__ == '__main__': 131 | G=nx.read_gml('..\datasets\Lesmiserables.gml') 132 | 133 | algorithm = GN_w(G) 134 | algorithm.run() 135 | algorithm.add_group() 136 | algorithm.draw_Q() 137 | algorithm.to_gml() 138 | 139 | -------------------------------------------------------------------------------- /algorithm/Girvan-Newman.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import networkx as nx 4 | import matplotlib.pyplot as plt 5 | import sys 6 | sys.path.append('../') 7 | 8 | class GN: 9 | def __init__(self, G): 10 | self.G_copy = G.copy() 11 | self.G = G 12 | self.partition = [[n for n in G.nodes()]] 13 | self.all_Q = [0.0] 14 | self.max_Q = 0.0 15 | 16 | #Using max_Q to divide communities 17 | def run(self): 18 | #Until there is no edge in the graph 19 | while len(self.G.edges()) != 0: 20 | #Find the most betweenness edge 21 | edge = max(nx.edge_betweenness_centrality(self.G).items(),key=lambda item:item[1])[0] 22 | #Remove the most betweenness edge 23 | self.G.remove_edge(edge[0], edge[1]) 24 | #List the the connected nodes 25 | components = [list(c) for c in list(nx.connected_components(self.G))] 26 | if len(components) != len(self.partition): 27 | #compute the Q 28 | cur_Q = self.cal_Q(components, self.G_copy) 29 | if cur_Q not in self.all_Q: 30 | self.all_Q.append(cur_Q) 31 | if cur_Q > self.max_Q: 32 | self.max_Q = cur_Q 33 | self.partition = components 34 | 35 | print('-----------the Max Q and divided communities-----------') 36 | print('The number of Communites:', len(self.partition)) 37 | print("Communites:", self.partition) 38 | print('Max_Q:', self.max_Q) 39 | return self.partition, self.all_Q, self.max_Q 40 | 41 | #Dividing communities by number 42 | def run_n(self, k): 43 | while len(self.G.edges()) != 0: 44 | edge = max(nx.edge_betweenness_centrality(self.G).items(),key=lambda item:item[1])[0] 45 | self.G.remove_edge(edge[0], edge[1]) 46 | components = [list(c) for c in list(nx.connected_components(self.G))] 47 | if len(components) <= k: 48 | cur_Q = self.cal_Q(components, self.G_copy) 49 | if cur_Q not in self.all_Q: 50 | self.all_Q.append(cur_Q) 51 | if cur_Q > self.max_Q: 52 | self.max_Q = cur_Q 53 | self.partition = components 54 | print('-----------Using number to divide communities and the Q-----------') 55 | print('The number of Communites', len(self.partition)) 56 | print("Communites: ", self.partition) 57 | print('Max_Q: ', self.max_Q) 58 | return self.partition, self.all_Q, self.max_Q 59 | 60 | #the process of divding the network 61 | #Return a list containing the result of each division, until each node is a community 62 | def run_to_all(self): 63 | divide = [] 64 | all_Q = [] 65 | while len(self.G.edges()) != 0: 66 | edge = max(nx.edge_betweenness_centrality(self.G).items(),key=lambda item:item[1])[0] 67 | self.G.remove_edge(edge[0], edge[1]) 68 | components = [list(c) for c in list(nx.connected_components(self.G))] 69 | if components not in divide: 70 | divide.append(components) 71 | cur_Q = self.cal_Q(components, self.G_copy) 72 | if cur_Q not in all_Q: 73 | all_Q.append(cur_Q) 74 | return divide, all_Q 75 | 76 | #Drawing the graph of Q and divided communities 77 | def draw_Q(self): 78 | plt.plot([x for x in range(1,len(self.G.nodes)+1)],self.all_Q) 79 | my_x_ticks = [x for x in range(1,len(self.G.nodes)+1)] 80 | plt.xticks(my_x_ticks) 81 | plt.axvline(len(self.partition),color='black',linestyle="--") 82 | plt.axvline(2, color='black',linestyle="--") 83 | #plt.axhline(self.all_Q[3],color='red',linestyle="--") 84 | plt.show() 85 | 86 | #Computing the Q 87 | def cal_Q(self,partition,G): 88 | m = len(G.edges(None, False)) 89 | a = [] 90 | e = [] 91 | 92 | for community in partition: 93 | t = 0.0 94 | for node in community: 95 | t += len([x for x in G.neighbors(node)]) 96 | a.append(t/(2*m)) 97 | 98 | for community in partition: 99 | t = 0.0 100 | for i in range(len(community)): 101 | for j in range(len(community)): 102 | if(G.has_edge(community[i], community[j])): 103 | t += 1.0 104 | e.append(t/(2*m)) 105 | 106 | q = 0.0 107 | for ei,ai in zip(e,a): 108 | q += (ei - ai**2) 109 | return q 110 | 111 | def add_group(self): 112 | num = 0 113 | nodegroup = {} 114 | for partition in self.partition: 115 | for node in partition: 116 | nodegroup[node] = {'group':num} 117 | num = num + 1 118 | nx.set_node_attributes(self.G_copy, nodegroup) 119 | 120 | def to_gml(self): 121 | nx.write_gml(self.G_copy, 'outputofGN.gml') 122 | 123 | if __name__ == '__main__': 124 | G=nx.read_gml('..\datasets\karate.gml',label='id') #Using max_Q to divide communities 125 | algorithm = GN(G) 126 | algorithm.run() 127 | algorithm.draw_Q() 128 | algorithm.add_group() 129 | algorithm.to_gml() 130 | 131 | G1=nx.read_gml('..\datasets\karate.gml',label='id') #Dividing communities by the number 132 | algorithmByNum = GN(G1) 133 | algorithmByNum.run_n(2) 134 | 135 | G2=nx.read_gml('karate.gml',label='id') #Dividing communities until each node is a community 136 | algorithmtoOne = GN(G2) 137 | some = algorithmtoOne.run_to_all() 138 | print('-----------the result of each division, until each node is a community----------') 139 | for i in range(1,len(G.nodes())+1): 140 | print('\n划分成{0}个社区：Q值为{1}'.format(i, some[1][i-1])) 141 | print(some[0][i-1]) 142 | 143 | -------------------------------------------------------------------------------- /algorithm/outputofGN.gml: -------------------------------------------------------------------------------- 1 | graph [ 2 | node [ 3 | id 0 4 | label "1" 5 | group 0 6 | ] 7 | node [ 8 | id 1 9 | label "2" 10 | group 0 11 | ] 12 | node [ 13 | id 2 14 | label "3" 15 | group 1 16 | ] 17 | node [ 18 | id 3 19 | label "4" 20 | group 0 21 | ] 22 | node [ 23 | id 4 24 | label "5" 25 | group 2 26 | ] 27 | node [ 28 | id 5 29 | label "6" 30 | group 2 31 | ] 32 | node [ 33 | id 6 34 | label "7" 35 | group 2 36 | ] 37 | node [ 38 | id 7 39 | label "8" 40 | group 0 41 | ] 42 | node [ 43 | id 8 44 | label "9" 45 | group 3 46 | ] 47 | node [ 48 | id 9 49 | label "10" 50 | group 4 51 | ] 52 | node [ 53 | id 10 54 | label "11" 55 | group 2 56 | ] 57 | node [ 58 | id 11 59 | label "12" 60 | group 0 61 | ] 62 | node [ 63 | id 12 64 | label "13" 65 | group 0 66 | ] 67 | node [ 68 | id 13 69 | label "14" 70 | group 0 71 | ] 72 | node [ 73 | id 14 74 | label "15" 75 | group 3 76 | ] 77 | node [ 78 | id 15 79 | label "16" 80 | group 3 81 | ] 82 | node [ 83 | id 16 84 | label "17" 85 | group 2 86 | ] 87 | node [ 88 | id 17 89 | label "18" 90 | group 0 91 | ] 92 | node [ 93 | id 18 94 | label "19" 95 | group 3 96 | ] 97 | node [ 98 | id 19 99 | label "20" 100 | group 0 101 | ] 102 | node [ 103 | id 20 104 | label "21" 105 | group 3 106 | ] 107 | node [ 108 | id 21 109 | label "22" 110 | group 0 111 | ] 112 | node [ 113 | id 22 114 | label "23" 115 | group 3 116 | ] 117 | node [ 118 | id 23 119 | label "24" 120 | group 3 121 | ] 122 | node [ 123 | id 24 124 | label "25" 125 | group 1 126 | ] 127 | node [ 128 | id 25 129 | label "26" 130 | group 1 131 | ] 132 | node [ 133 | id 26 134 | label "27" 135 | group 3 136 | ] 137 | node [ 138 | id 27 139 | label "28" 140 | group 1 141 | ] 142 | node [ 143 | id 28 144 | label "29" 145 | group 1 146 | ] 147 | node [ 148 | id 29 149 | label "30" 150 | group 3 151 | ] 152 | node [ 153 | id 30 154 | label "31" 155 | group 3 156 | ] 157 | node [ 158 | id 31 159 | label "32" 160 | group 1 161 | ] 162 | node [ 163 | id 32 164 | label "33" 165 | group 3 166 | ] 167 | node [ 168 | id 33 169 | label "34" 170 | group 3 171 | ] 172 | edge [ 173 | source 0 174 | target 1 175 | ] 176 | edge [ 177 | source 0 178 | target 2 179 | ] 180 | edge [ 181 | source 0 182 | target 3 183 | ] 184 | edge [ 185 | source 0 186 | target 4 187 | ] 188 | edge [ 189 | source 0 190 | target 5 191 | ] 192 | edge [ 193 | source 0 194 | target 6 195 | ] 196 | edge [ 197 | source 0 198 | target 7 199 | ] 200 | edge [ 201 | source 0 202 | target 8 203 | ] 204 | edge [ 205 | source 0 206 | target 10 207 | ] 208 | edge [ 209 | source 0 210 | target 11 211 | ] 212 | edge [ 213 | source 0 214 | target 12 215 | ] 216 | edge [ 217 | source 0 218 | target 13 219 | ] 220 | edge [ 221 | source 0 222 | target 17 223 | ] 224 | edge [ 225 | source 0 226 | target 19 227 | ] 228 | edge [ 229 | source 0 230 | target 21 231 | ] 232 | edge [ 233 | source 0 234 | target 31 235 | ] 236 | edge [ 237 | source 1 238 | target 2 239 | ] 240 | edge [ 241 | source 1 242 | target 3 243 | ] 244 | edge [ 245 | source 1 246 | target 7 247 | ] 248 | edge [ 249 | source 1 250 | target 13 251 | ] 252 | edge [ 253 | source 1 254 | target 17 255 | ] 256 | edge [ 257 | source 1 258 | target 19 259 | ] 260 | edge [ 261 | source 1 262 | target 21 263 | ] 264 | edge [ 265 | source 1 266 | target 30 267 | ] 268 | edge [ 269 | source 2 270 | target 3 271 | ] 272 | edge [ 273 | source 2 274 | target 7 275 | ] 276 | edge [ 277 | source 2 278 | target 8 279 | ] 280 | edge [ 281 | source 2 282 | target 9 283 | ] 284 | edge [ 285 | source 2 286 | target 13 287 | ] 288 | edge [ 289 | source 2 290 | target 27 291 | ] 292 | edge [ 293 | source 2 294 | target 28 295 | ] 296 | edge [ 297 | source 2 298 | target 32 299 | ] 300 | edge [ 301 | source 3 302 | target 7 303 | ] 304 | edge [ 305 | source 3 306 | target 12 307 | ] 308 | edge [ 309 | source 3 310 | target 13 311 | ] 312 | edge [ 313 | source 4 314 | target 6 315 | ] 316 | edge [ 317 | source 4 318 | target 10 319 | ] 320 | edge [ 321 | source 5 322 | target 6 323 | ] 324 | edge [ 325 | source 5 326 | target 10 327 | ] 328 | edge [ 329 | source 5 330 | target 16 331 | ] 332 | edge [ 333 | source 6 334 | target 16 335 | ] 336 | edge [ 337 | source 8 338 | target 30 339 | ] 340 | edge [ 341 | source 8 342 | target 32 343 | ] 344 | edge [ 345 | source 8 346 | target 33 347 | ] 348 | edge [ 349 | source 9 350 | target 33 351 | ] 352 | edge [ 353 | source 13 354 | target 33 355 | ] 356 | edge [ 357 | source 14 358 | target 32 359 | ] 360 | edge [ 361 | source 14 362 | target 33 363 | ] 364 | edge [ 365 | source 15 366 | target 32 367 | ] 368 | edge [ 369 | source 15 370 | target 33 371 | ] 372 | edge [ 373 | source 18 374 | target 32 375 | ] 376 | edge [ 377 | source 18 378 | target 33 379 | ] 380 | edge [ 381 | source 19 382 | target 33 383 | ] 384 | edge [ 385 | source 20 386 | target 32 387 | ] 388 | edge [ 389 | source 20 390 | target 33 391 | ] 392 | edge [ 393 | source 22 394 | target 32 395 | ] 396 | edge [ 397 | source 22 398 | target 33 399 | ] 400 | edge [ 401 | source 23 402 | target 25 403 | ] 404 | edge [ 405 | source 23 406 | target 27 407 | ] 408 | edge [ 409 | source 23 410 | target 29 411 | ] 412 | edge [ 413 | source 23 414 | target 32 415 | ] 416 | edge [ 417 | source 23 418 | target 33 419 | ] 420 | edge [ 421 | source 24 422 | target 25 423 | ] 424 | edge [ 425 | source 24 426 | target 27 427 | ] 428 | edge [ 429 | source 24 430 | target 31 431 | ] 432 | edge [ 433 | source 25 434 | target 31 435 | ] 436 | edge [ 437 | source 26 438 | target 29 439 | ] 440 | edge [ 441 | source 26 442 | target 33 443 | ] 444 | edge [ 445 | source 27 446 | target 33 447 | ] 448 | edge [ 449 | source 28 450 | target 31 451 | ] 452 | edge [ 453 | source 28 454 | target 33 455 | ] 456 | edge [ 457 | source 29 458 | target 32 459 | ] 460 | edge [ 461 | source 29 462 | target 33 463 | ] 464 | edge [ 465 | source 30 466 | target 32 467 | ] 468 | edge [ 469 | source 30 470 | target 33 471 | ] 472 | edge [ 473 | source 31 474 | target 32 475 | ] 476 | edge [ 477 | source 31 478 | target 33 479 | ] 480 | edge [ 481 | source 32 482 | target 33 483 | ] 484 | ] 485 | -------------------------------------------------------------------------------- /algorithm/outputofGN_weighted.gml: -------------------------------------------------------------------------------- 1 | graph [ 2 | node [ 3 | id 0 4 | label "Myriel" 5 | group 0 6 | ] 7 | node [ 8 | id 1 9 | label "Napoleon" 10 | group 0 11 | ] 12 | node [ 13 | id 2 14 | label "MlleBaptistine" 15 | group 0 16 | ] 17 | node [ 18 | id 3 19 | label "MmeMagloire" 20 | group 0 21 | ] 22 | node [ 23 | id 4 24 | label "CountessDeLo" 25 | group 0 26 | ] 27 | node [ 28 | id 5 29 | label "Geborand" 30 | group 0 31 | ] 32 | node [ 33 | id 6 34 | label "Champtercier" 35 | group 0 36 | ] 37 | node [ 38 | id 7 39 | label "Cravatte" 40 | group 0 41 | ] 42 | node [ 43 | id 8 44 | label "Count" 45 | group 0 46 | ] 47 | node [ 48 | id 9 49 | label "OldMan" 50 | group 0 51 | ] 52 | node [ 53 | id 10 54 | label "Labarre" 55 | group 1 56 | ] 57 | node [ 58 | id 11 59 | label "Valjean" 60 | group 2 61 | ] 62 | node [ 63 | id 12 64 | label "Marguerite" 65 | group 3 66 | ] 67 | node [ 68 | id 13 69 | label "MmeDeR" 70 | group 4 71 | ] 72 | node [ 73 | id 14 74 | label "Isabeau" 75 | group 5 76 | ] 77 | node [ 78 | id 15 79 | label "Gervais" 80 | group 6 81 | ] 82 | node [ 83 | id 16 84 | label "Tholomyes" 85 | group 3 86 | ] 87 | node [ 88 | id 17 89 | label "Listolier" 90 | group 3 91 | ] 92 | node [ 93 | id 18 94 | label "Fameuil" 95 | group 3 96 | ] 97 | node [ 98 | id 19 99 | label "Blacheville" 100 | group 3 101 | ] 102 | node [ 103 | id 20 104 | label "Favourite" 105 | group 3 106 | ] 107 | node [ 108 | id 21 109 | label "Dahlia" 110 | group 3 111 | ] 112 | node [ 113 | id 22 114 | label "Zephine" 115 | group 3 116 | ] 117 | node [ 118 | id 23 119 | label "Fantine" 120 | group 3 121 | ] 122 | node [ 123 | id 24 124 | label "MmeThenardier" 125 | group 2 126 | ] 127 | node [ 128 | id 25 129 | label "Thenardier" 130 | group 2 131 | ] 132 | node [ 133 | id 26 134 | label "Cosette" 135 | group 2 136 | ] 137 | node [ 138 | id 27 139 | label "Javert" 140 | group 2 141 | ] 142 | node [ 143 | id 28 144 | label "Fauchelevent" 145 | group 2 146 | ] 147 | node [ 148 | id 29 149 | label "Bamatabois" 150 | group 7 151 | ] 152 | node [ 153 | id 30 154 | label "Perpetue" 155 | group 8 156 | ] 157 | node [ 158 | id 31 159 | label "Simplice" 160 | group 2 161 | ] 162 | node [ 163 | id 32 164 | label "Scaufflaire" 165 | group 9 166 | ] 167 | node [ 168 | id 33 169 | label "Woman1" 170 | group 2 171 | ] 172 | node [ 173 | id 34 174 | label "Judge" 175 | group 7 176 | ] 177 | node [ 178 | id 35 179 | label "Champmathieu" 180 | group 7 181 | ] 182 | node [ 183 | id 36 184 | label "Brevet" 185 | group 7 186 | ] 187 | node [ 188 | id 37 189 | label "Chenildieu" 190 | group 7 191 | ] 192 | node [ 193 | id 38 194 | label "Cochepaille" 195 | group 7 196 | ] 197 | node [ 198 | id 39 199 | label "Pontmercy" 200 | group 10 201 | ] 202 | node [ 203 | id 40 204 | label "Boulatruelle" 205 | group 11 206 | ] 207 | node [ 208 | id 41 209 | label "Eponine" 210 | group 2 211 | ] 212 | node [ 213 | id 42 214 | label "Anzelma" 215 | group 2 216 | ] 217 | node [ 218 | id 43 219 | label "Woman2" 220 | group 2 221 | ] 222 | node [ 223 | id 44 224 | label "MotherInnocent" 225 | group 2 226 | ] 227 | node [ 228 | id 45 229 | label "Gribier" 230 | group 12 231 | ] 232 | node [ 233 | id 46 234 | label "Jondrette" 235 | group 13 236 | ] 237 | node [ 238 | id 47 239 | label "MmeBurgon" 240 | group 14 241 | ] 242 | node [ 243 | id 48 244 | label "Gavroche" 245 | group 14 246 | ] 247 | node [ 248 | id 49 249 | label "Gillenormand" 250 | group 2 251 | ] 252 | node [ 253 | id 50 254 | label "Magnon" 255 | group 15 256 | ] 257 | node [ 258 | id 51 259 | label "MlleGillenormand" 260 | group 2 261 | ] 262 | node [ 263 | id 52 264 | label "MmePontmercy" 265 | group 10 266 | ] 267 | node [ 268 | id 53 269 | label "MlleVaubois" 270 | group 16 271 | ] 272 | node [ 273 | id 54 274 | label "LtGillenormand" 275 | group 17 276 | ] 277 | node [ 278 | id 55 279 | label "Marius" 280 | group 2 281 | ] 282 | node [ 283 | id 56 284 | label "BaronessT" 285 | group 18 286 | ] 287 | node [ 288 | id 57 289 | label "Mabeuf" 290 | group 14 291 | ] 292 | node [ 293 | id 58 294 | label "Enjolras" 295 | group 14 296 | ] 297 | node [ 298 | id 59 299 | label "Combeferre" 300 | group 14 301 | ] 302 | node [ 303 | id 60 304 | label "Prouvaire" 305 | group 14 306 | ] 307 | node [ 308 | id 61 309 | label "Feuilly" 310 | group 14 311 | ] 312 | node [ 313 | id 62 314 | label "Courfeyrac" 315 | group 14 316 | ] 317 | node [ 318 | id 63 319 | label "Bahorel" 320 | group 14 321 | ] 322 | node [ 323 | id 64 324 | label "Bossuet" 325 | group 14 326 | ] 327 | node [ 328 | id 65 329 | label "Joly" 330 | group 14 331 | ] 332 | node [ 333 | id 66 334 | label "Grantaire" 335 | group 14 336 | ] 337 | node [ 338 | id 67 339 | label "MotherPlutarch" 340 | group 14 341 | ] 342 | node [ 343 | id 68 344 | label "Gueulemer" 345 | group 2 346 | ] 347 | node [ 348 | id 69 349 | label "Babet" 350 | group 2 351 | ] 352 | node [ 353 | id 70 354 | label "Claquesous" 355 | group 2 356 | ] 357 | node [ 358 | id 71 359 | label "Montparnasse" 360 | group 2 361 | ] 362 | node [ 363 | id 72 364 | label "Toussaint" 365 | group 19 366 | ] 367 | node [ 368 | id 73 369 | label "Child1" 370 | group 14 371 | ] 372 | node [ 373 | id 74 374 | label "Child2" 375 | group 14 376 | ] 377 | node [ 378 | id 75 379 | label "Brujon" 380 | group 2 381 | ] 382 | node [ 383 | id 76 384 | label "MmeHucheloup" 385 | group 14 386 | ] 387 | edge [ 388 | source 0 389 | target 1 390 | value 1 391 | ] 392 | edge [ 393 | source 0 394 | target 2 395 | value 8 396 | ] 397 | edge [ 398 | source 0 399 | target 3 400 | value 10 401 | ] 402 | edge [ 403 | source 0 404 | target 4 405 | value 1 406 | ] 407 | edge [ 408 | source 0 409 | target 5 410 | value 1 411 | ] 412 | edge [ 413 | source 0 414 | target 6 415 | value 1 416 | ] 417 | edge [ 418 | source 0 419 | target 7 420 | value 1 421 | ] 422 | edge [ 423 | source 0 424 | target 8 425 | value 2 426 | ] 427 | edge [ 428 | source 0 429 | target 9 430 | value 1 431 | ] 432 | edge [ 433 | source 0 434 | target 11 435 | value 5 436 | ] 437 | edge [ 438 | source 2 439 | target 3 440 | value 6 441 | ] 442 | edge [ 443 | source 2 444 | target 11 445 | value 3 446 | ] 447 | edge [ 448 | source 3 449 | target 11 450 | value 3 451 | ] 452 | edge [ 453 | source 10 454 | target 11 455 | value 1 456 | ] 457 | edge [ 458 | source 11 459 | 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58 1399 | target 61 1400 | value 6 1401 | ] 1402 | edge [ 1403 | source 58 1404 | target 62 1405 | value 17 1406 | ] 1407 | edge [ 1408 | source 58 1409 | target 63 1410 | value 4 1411 | ] 1412 | edge [ 1413 | source 58 1414 | target 64 1415 | value 10 1416 | ] 1417 | edge [ 1418 | source 58 1419 | target 65 1420 | value 5 1421 | ] 1422 | edge [ 1423 | source 58 1424 | target 66 1425 | value 3 1426 | ] 1427 | edge [ 1428 | source 58 1429 | target 70 1430 | value 1 1431 | ] 1432 | edge [ 1433 | source 58 1434 | target 76 1435 | value 1 1436 | ] 1437 | edge [ 1438 | source 59 1439 | target 60 1440 | value 2 1441 | ] 1442 | edge [ 1443 | source 59 1444 | target 61 1445 | value 5 1446 | ] 1447 | edge [ 1448 | source 59 1449 | target 62 1450 | value 13 1451 | ] 1452 | edge [ 1453 | source 59 1454 | target 63 1455 | value 5 1456 | ] 1457 | edge [ 1458 | source 59 1459 | target 64 1460 | value 9 1461 | ] 1462 | edge [ 1463 | source 59 1464 | target 65 1465 | value 5 1466 | ] 1467 | edge [ 1468 | source 59 1469 | target 66 1470 | value 1 1471 | ] 1472 | edge [ 1473 | source 60 1474 | target 61 1475 | value 2 1476 | ] 1477 | edge [ 1478 | source 60 1479 | target 62 1480 | value 3 1481 | ] 1482 | edge [ 1483 | source 60 1484 | target 63 1485 | value 2 1486 | ] 1487 | edge [ 1488 | source 60 1489 | target 64 1490 | value 2 1491 | ] 1492 | edge [ 1493 | source 60 1494 | target 65 1495 | value 2 1496 | ] 1497 | edge [ 1498 | source 60 1499 | target 66 1500 | value 1 1501 | ] 1502 | edge [ 1503 | source 61 1504 | target 62 1505 | value 6 1506 | ] 1507 | edge [ 1508 | source 61 1509 | target 63 1510 | value 3 1511 | ] 1512 | edge [ 1513 | source 61 1514 | target 64 1515 | value 6 1516 | ] 1517 | edge [ 1518 | source 61 1519 | target 65 1520 | value 5 1521 | ] 1522 | edge [ 1523 | source 61 1524 | target 66 1525 | value 1 1526 | ] 1527 | edge [ 1528 | source 62 1529 | target 63 1530 | value 6 1531 | ] 1532 | edge [ 1533 | source 62 1534 | target 64 1535 | value 12 1536 | ] 1537 | edge [ 1538 | source 62 1539 | target 65 1540 | value 5 1541 | ] 1542 | edge [ 1543 | source 62 1544 | target 66 1545 | value 2 1546 | ] 1547 | edge [ 1548 | source 62 1549 | target 76 1550 | value 1 1551 | ] 1552 | edge [ 1553 | source 63 1554 | target 64 1555 | value 4 1556 | ] 1557 | edge [ 1558 | source 63 1559 | target 65 1560 | value 5 1561 | ] 1562 | edge [ 1563 | source 63 1564 | target 66 1565 | value 1 1566 | ] 1567 | edge [ 1568 | source 63 1569 | target 76 1570 | value 1 1571 | ] 1572 | edge [ 1573 | source 64 1574 | target 65 1575 | value 7 1576 | ] 1577 | edge [ 1578 | source 64 1579 | target 66 1580 | value 3 1581 | ] 1582 | edge [ 1583 | source 64 1584 | target 76 1585 | value 1 1586 | ] 1587 | edge [ 1588 | source 65 1589 | target 66 1590 | value 2 1591 | ] 1592 | edge [ 1593 | source 65 1594 | target 76 1595 | value 1 1596 | ] 1597 | edge [ 1598 | source 66 1599 | target 76 1600 | value 1 1601 | ] 1602 | edge [ 1603 | source 68 1604 | target 69 1605 | value 6 1606 | ] 1607 | edge [ 1608 | source 68 1609 | target 70 1610 | value 4 1611 | ] 1612 | edge [ 1613 | source 68 1614 | target 71 1615 | value 2 1616 | ] 1617 | edge [ 1618 | source 68 1619 | target 75 1620 | value 3 1621 | ] 1622 | edge [ 1623 | source 69 1624 | target 70 1625 | value 4 1626 | ] 1627 | edge [ 1628 | source 69 1629 | target 71 1630 | value 2 1631 | ] 1632 | edge [ 1633 | source 69 1634 | target 75 1635 | value 3 1636 | ] 1637 | edge [ 1638 | source 70 1639 | target 71 1640 | value 2 1641 | ] 1642 | edge [ 1643 | source 70 1644 | target 75 1645 | value 1 1646 | ] 1647 | edge [ 1648 | source 71 1649 | target 75 1650 | value 1 1651 | ] 1652 | edge [ 1653 | source 73 1654 | target 74 1655 | value 3 1656 | ] 1657 | ] 1658 | -------------------------------------------------------------------------------- /datasets/karate.gml: -------------------------------------------------------------------------------- 1 | Creator "Mark Newman on Fri Jul 21 12:39:27 2006" 2 | graph 3 | [ 4 | node 5 | [ 6 | id 1 7 | ] 8 | node 9 | [ 10 | id 2 11 | ] 12 | node 13 | [ 14 | id 3 15 | ] 16 | node 17 | [ 18 | id 4 19 | ] 20 | node 21 | [ 22 | id 5 23 | ] 24 | node 25 | [ 26 | id 6 27 | ] 28 | node 29 | [ 30 | id 7 31 | ] 32 | node 33 | [ 34 | id 8 35 | ] 36 | node 37 | [ 38 | id 9 39 | ] 40 | node 41 | [ 42 | id 10 43 | ] 44 | node 45 | [ 46 | id 11 47 | ] 48 | node 49 | [ 50 | id 12 51 | ] 52 | node 53 | [ 54 | id 13 55 | ] 56 | node 57 | [ 58 | id 14 59 | ] 60 | node 61 | [ 62 | id 15 63 | ] 64 | node 65 | [ 66 | id 16 67 | ] 68 | node 69 | [ 70 | id 17 71 | ] 72 | node 73 | [ 74 | id 18 75 | ] 76 | node 77 | [ 78 | id 19 79 | ] 80 | node 81 | [ 82 | id 20 83 | ] 84 | node 85 | [ 86 | id 21 87 | ] 88 | node 89 | [ 90 | id 22 91 | ] 92 | node 93 | [ 94 | id 23 95 | ] 96 | node 97 | [ 98 | id 24 99 | ] 100 | node 101 | [ 102 | id 25 103 | ] 104 | node 105 | [ 106 | id 26 107 | ] 108 | node 109 | [ 110 | id 27 111 | ] 112 | node 113 | [ 114 | id 28 115 | ] 116 | node 117 | [ 118 | id 29 119 | ] 120 | node 121 | [ 122 | id 30 123 | ] 124 | node 125 | [ 126 | id 31 127 | ] 128 | node 129 | [ 130 | id 32 131 | ] 132 | node 133 | [ 134 | id 33 135 | ] 136 | node 137 | [ 138 | id 34 139 | ] 140 | edge 141 | [ 142 | source 2 143 | target 1 144 | ] 145 | edge 146 | [ 147 | source 3 148 | target 1 149 | ] 150 | edge 151 | [ 152 | source 3 153 | target 2 154 | ] 155 | edge 156 | [ 157 | source 4 158 | target 1 159 | ] 160 | edge 161 | [ 162 | source 4 163 | target 2 164 | ] 165 | edge 166 | [ 167 | source 4 168 | target 3 169 | ] 170 | edge 171 | [ 172 | source 5 173 | target 1 174 | ] 175 | edge 176 | [ 177 | source 6 178 | target 1 179 | ] 180 | edge 181 | [ 182 | source 7 183 | target 1 184 | ] 185 | edge 186 | [ 187 | source 7 188 | target 5 189 | ] 190 | edge 191 | [ 192 | source 7 193 | target 6 194 | ] 195 | edge 196 | [ 197 | source 8 198 | target 1 199 | ] 200 | edge 201 | [ 202 | source 8 203 | target 2 204 | ] 205 | edge 206 | [ 207 | source 8 208 | target 3 209 | ] 210 | edge 211 | [ 212 | source 8 213 | target 4 214 | ] 215 | edge 216 | [ 217 | source 9 218 | target 1 219 | ] 220 | edge 221 | [ 222 | source 9 223 | target 3 224 | ] 225 | edge 226 | [ 227 | source 10 228 | target 3 229 | ] 230 | edge 231 | [ 232 | source 11 233 | target 1 234 | ] 235 | edge 236 | [ 237 | source 11 238 | target 5 239 | ] 240 | edge 241 | [ 242 | source 11 243 | target 6 244 | ] 245 | edge 246 | [ 247 | source 12 248 | target 1 249 | ] 250 | edge 251 | [ 252 | source 13 253 | target 1 254 | ] 255 | edge 256 | [ 257 | source 13 258 | target 4 259 | ] 260 | edge 261 | [ 262 | source 14 263 | target 1 264 | ] 265 | edge 266 | [ 267 | source 14 268 | target 2 269 | ] 270 | edge 271 | [ 272 | source 14 273 | target 3 274 | ] 275 | edge 276 | [ 277 | source 14 278 | target 4 279 | ] 280 | edge 281 | [ 282 | source 17 283 | target 6 284 | ] 285 | edge 286 | [ 287 | source 17 288 | target 7 289 | ] 290 | edge 291 | [ 292 | source 18 293 | target 1 294 | ] 295 | edge 296 | [ 297 | source 18 298 | target 2 299 | ] 300 | edge 301 | [ 302 | source 20 303 | target 1 304 | ] 305 | edge 306 | [ 307 | source 20 308 | target 2 309 | ] 310 | edge 311 | [ 312 | source 22 313 | target 1 314 | ] 315 | edge 316 | [ 317 | source 22 318 | target 2 319 | ] 320 | edge 321 | [ 322 | source 26 323 | target 24 324 | ] 325 | edge 326 | [ 327 | source 26 328 | target 25 329 | ] 330 | edge 331 | [ 332 | source 28 333 | target 3 334 | ] 335 | edge 336 | [ 337 | source 28 338 | target 24 339 | ] 340 | edge 341 | [ 342 | source 28 343 | target 25 344 | ] 345 | edge 346 | [ 347 | source 29 348 | target 3 349 | ] 350 | edge 351 | [ 352 | source 30 353 | target 24 354 | ] 355 | edge 356 | [ 357 | source 30 358 | target 27 359 | ] 360 | edge 361 | [ 362 | source 31 363 | target 2 364 | ] 365 | edge 366 | [ 367 | source 31 368 | target 9 369 | ] 370 | edge 371 | [ 372 | source 32 373 | target 1 374 | ] 375 | edge 376 | [ 377 | source 32 378 | target 25 379 | ] 380 | edge 381 | [ 382 | source 32 383 | target 26 384 | ] 385 | edge 386 | [ 387 | source 32 388 | target 29 389 | ] 390 | edge 391 | [ 392 | source 33 393 | target 3 394 | ] 395 | edge 396 | [ 397 | source 33 398 | target 9 399 | ] 400 | edge 401 | [ 402 | source 33 403 | target 15 404 | ] 405 | edge 406 | [ 407 | source 33 408 | target 16 409 | ] 410 | edge 411 | [ 412 | source 33 413 | target 19 414 | ] 415 | edge 416 | [ 417 | source 33 418 | target 21 419 | ] 420 | edge 421 | [ 422 | source 33 423 | target 23 424 | ] 425 | edge 426 | [ 427 | source 33 428 | target 24 429 | ] 430 | edge 431 | [ 432 | source 33 433 | target 30 434 | ] 435 | edge 436 | [ 437 | source 33 438 | target 31 439 | ] 440 | edge 441 | [ 442 | source 33 443 | target 32 444 | ] 445 | edge 446 | [ 447 | source 34 448 | target 9 449 | ] 450 | edge 451 | [ 452 | source 34 453 | target 10 454 | ] 455 | edge 456 | [ 457 | source 34 458 | target 14 459 | ] 460 | edge 461 | [ 462 | source 34 463 | target 15 464 | ] 465 | edge 466 | [ 467 | source 34 468 | target 16 469 | ] 470 | edge 471 | [ 472 | source 34 473 | target 19 474 | ] 475 | edge 476 | [ 477 | source 34 478 | target 20 479 | ] 480 | edge 481 | [ 482 | source 34 483 | target 21 484 | ] 485 | edge 486 | [ 487 | source 34 488 | target 23 489 | ] 490 | edge 491 | [ 492 | source 34 493 | target 24 494 | ] 495 | edge 496 | [ 497 | source 34 498 | target 27 499 | ] 500 | edge 501 | [ 502 | source 34 503 | target 28 504 | ] 505 | edge 506 | [ 507 | source 34 508 | target 29 509 | ] 510 | edge 511 | [ 512 | source 34 513 | target 30 514 | ] 515 | edge 516 | [ 517 | source 34 518 | target 31 519 | ] 520 | edge 521 | [ 522 | source 34 523 | target 32 524 | ] 525 | edge 526 | [ 527 | source 34 528 | target 33 529 | ] 530 | ] 531 | -------------------------------------------------------------------------------- /datasets/lesmiserables.gml: -------------------------------------------------------------------------------- 1 | Creator "Mark Newman on Fri Jul 21 12:44:53 2006" 2 | graph 3 | [ 4 | node 5 | [ 6 | id 0 7 | label "Myriel" 8 | ] 9 | node 10 | [ 11 | id 1 12 | label "Napoleon" 13 | ] 14 | node 15 | [ 16 | id 2 17 | label "MlleBaptistine" 18 | ] 19 | node 20 | [ 21 | id 3 22 | label "MmeMagloire" 23 | ] 24 | node 25 | [ 26 | id 4 27 | label "CountessDeLo" 28 | ] 29 | node 30 | [ 31 | id 5 32 | label "Geborand" 33 | ] 34 | node 35 | [ 36 | id 6 37 | label "Champtercier" 38 | ] 39 | node 40 | [ 41 | id 7 42 | label "Cravatte" 43 | ] 44 | node 45 | [ 46 | id 8 47 | label "Count" 48 | ] 49 | node 50 | [ 51 | id 9 52 | label "OldMan" 53 | ] 54 | node 55 | [ 56 | id 10 57 | label "Labarre" 58 | ] 59 | node 60 | [ 61 | id 11 62 | label "Valjean" 63 | ] 64 | node 65 | [ 66 | id 12 67 | label "Marguerite" 68 | ] 69 | node 70 | [ 71 | id 13 72 | label "MmeDeR" 73 | ] 74 | node 75 | [ 76 | id 14 77 | label "Isabeau" 78 | ] 79 | node 80 | [ 81 | id 15 82 | label "Gervais" 83 | ] 84 | node 85 | [ 86 | id 16 87 | label "Tholomyes" 88 | ] 89 | node 90 | [ 91 | id 17 92 | label "Listolier" 93 | ] 94 | node 95 | [ 96 | id 18 97 | label "Fameuil" 98 | ] 99 | node 100 | [ 101 | id 19 102 | label "Blacheville" 103 | ] 104 | node 105 | [ 106 | id 20 107 | label "Favourite" 108 | ] 109 | node 110 | [ 111 | id 21 112 | label "Dahlia" 113 | ] 114 | node 115 | [ 116 | id 22 117 | label "Zephine" 118 | ] 119 | node 120 | [ 121 | id 23 122 | label "Fantine" 123 | ] 124 | node 125 | [ 126 | id 24 127 | label "MmeThenardier" 128 | ] 129 | node 130 | [ 131 | id 25 132 | label "Thenardier" 133 | ] 134 | node 135 | [ 136 | id 26 137 | label "Cosette" 138 | ] 139 | node 140 | [ 141 | id 27 142 | label "Javert" 143 | ] 144 | node 145 | [ 146 | id 28 147 | label "Fauchelevent" 148 | ] 149 | node 150 | [ 151 | id 29 152 | label "Bamatabois" 153 | ] 154 | node 155 | [ 156 | id 30 157 | label "Perpetue" 158 | ] 159 | node 160 | [ 161 | id 31 162 | label "Simplice" 163 | ] 164 | node 165 | [ 166 | id 32 167 | label "Scaufflaire" 168 | ] 169 | node 170 | [ 171 | id 33 172 | label "Woman1" 173 | ] 174 | node 175 | [ 176 | id 34 177 | label "Judge" 178 | ] 179 | node 180 | [ 181 | id 35 182 | label "Champmathieu" 183 | ] 184 | node 185 | [ 186 | id 36 187 | label "Brevet" 188 | ] 189 | node 190 | [ 191 | id 37 192 | label "Chenildieu" 193 | ] 194 | node 195 | [ 196 | id 38 197 | label "Cochepaille" 198 | ] 199 | node 200 | [ 201 | id 39 202 | label "Pontmercy" 203 | ] 204 | node 205 | [ 206 | id 40 207 | label "Boulatruelle" 208 | ] 209 | node 210 | [ 211 | id 41 212 | label "Eponine" 213 | ] 214 | node 215 | [ 216 | id 42 217 | label "Anzelma" 218 | ] 219 | node 220 | [ 221 | id 43 222 | label "Woman2" 223 | ] 224 | node 225 | [ 226 | id 44 227 | label "MotherInnocent" 228 | ] 229 | node 230 | [ 231 | id 45 232 | label "Gribier" 233 | ] 234 | node 235 | [ 236 | id 46 237 | label "Jondrette" 238 | ] 239 | node 240 | [ 241 | id 47 242 | label "MmeBurgon" 243 | ] 244 | node 245 | [ 246 | id 48 247 | label "Gavroche" 248 | ] 249 | node 250 | [ 251 | id 49 252 | label "Gillenormand" 253 | ] 254 | node 255 | [ 256 | id 50 257 | label "Magnon" 258 | ] 259 | node 260 | [ 261 | id 51 262 | label "MlleGillenormand" 263 | ] 264 | node 265 | [ 266 | id 52 267 | label "MmePontmercy" 268 | ] 269 | node 270 | [ 271 | id 53 272 | label "MlleVaubois" 273 | ] 274 | node 275 | [ 276 | id 54 277 | label "LtGillenormand" 278 | ] 279 | node 280 | [ 281 | id 55 282 | label "Marius" 283 | ] 284 | node 285 | [ 286 | id 56 287 | label "BaronessT" 288 | ] 289 | node 290 | [ 291 | id 57 292 | label "Mabeuf" 293 | ] 294 | node 295 | [ 296 | id 58 297 | label "Enjolras" 298 | ] 299 | node 300 | [ 301 | id 59 302 | label "Combeferre" 303 | ] 304 | node 305 | [ 306 | id 60 307 | label "Prouvaire" 308 | ] 309 | node 310 | [ 311 | id 61 312 | label "Feuilly" 313 | ] 314 | node 315 | [ 316 | id 62 317 | label "Courfeyrac" 318 | ] 319 | node 320 | [ 321 | id 63 322 | label "Bahorel" 323 | ] 324 | node 325 | [ 326 | id 64 327 | label "Bossuet" 328 | ] 329 | node 330 | [ 331 | id 65 332 | label "Joly" 333 | ] 334 | node 335 | [ 336 | id 66 337 | label "Grantaire" 338 | ] 339 | node 340 | [ 341 | id 67 342 | label "MotherPlutarch" 343 | ] 344 | node 345 | [ 346 | id 68 347 | label "Gueulemer" 348 | ] 349 | node 350 | [ 351 | id 69 352 | label "Babet" 353 | ] 354 | node 355 | [ 356 | id 70 357 | label "Claquesous" 358 | ] 359 | node 360 | [ 361 | id 71 362 | label "Montparnasse" 363 | ] 364 | node 365 | [ 366 | id 72 367 | label "Toussaint" 368 | ] 369 | node 370 | [ 371 | id 73 372 | label "Child1" 373 | ] 374 | node 375 | [ 376 | id 74 377 | label "Child2" 378 | ] 379 | node 380 | [ 381 | id 75 382 | label "Brujon" 383 | ] 384 | node 385 | [ 386 | id 76 387 | label "MmeHucheloup" 388 | ] 389 | edge 390 | [ 391 | source 1 392 | target 0 393 | value 1 394 | ] 395 | edge 396 | [ 397 | source 2 398 | target 0 399 | value 8 400 | ] 401 | edge 402 | [ 403 | source 3 404 | target 0 405 | value 10 406 | ] 407 | edge 408 | [ 409 | source 3 410 | target 2 411 | value 6 412 | ] 413 | edge 414 | [ 415 | source 4 416 | target 0 417 | value 1 418 | ] 419 | edge 420 | [ 421 | source 5 422 | target 0 423 | value 1 424 | ] 425 | edge 426 | [ 427 | source 6 428 | target 0 429 | value 1 430 | ] 431 | edge 432 | [ 433 | source 7 434 | target 0 435 | value 1 436 | ] 437 | edge 438 | [ 439 | source 8 440 | target 0 441 | value 2 442 | ] 443 | edge 444 | [ 445 | source 9 446 | target 0 447 | value 1 448 | ] 449 | edge 450 | [ 451 | source 11 452 | target 10 453 | value 1 454 | ] 455 | edge 456 | [ 457 | source 11 458 | target 3 459 | value 3 460 | ] 461 | edge 462 | [ 463 | source 11 464 | target 2 465 | value 3 466 | ] 467 | edge 468 | [ 469 | source 11 470 | target 0 471 | value 5 472 | ] 473 | edge 474 | [ 475 | source 12 476 | target 11 477 | value 1 478 | ] 479 | edge 480 | [ 481 | source 13 482 | target 11 483 | value 1 484 | ] 485 | edge 486 | [ 487 | source 14 488 | target 11 489 | value 1 490 | ] 491 | edge 492 | [ 493 | source 15 494 | target 11 495 | value 1 496 | ] 497 | edge 498 | [ 499 | source 17 500 | target 16 501 | value 4 502 | ] 503 | edge 504 | [ 505 | source 18 506 | target 16 507 | value 4 508 | ] 509 | edge 510 | [ 511 | source 18 512 | target 17 513 | value 4 514 | ] 515 | edge 516 | [ 517 | source 19 518 | target 16 519 | value 4 520 | ] 521 | edge 522 | [ 523 | source 19 524 | target 17 525 | value 4 526 | ] 527 | edge 528 | [ 529 | source 19 530 | target 18 531 | value 4 532 | ] 533 | edge 534 | [ 535 | source 20 536 | target 16 537 | value 3 538 | ] 539 | edge 540 | [ 541 | source 20 542 | target 17 543 | value 3 544 | ] 545 | edge 546 | [ 547 | source 20 548 | target 18 549 | value 3 550 | ] 551 | edge 552 | [ 553 | source 20 554 | target 19 555 | value 4 556 | ] 557 | edge 558 | [ 559 | source 21 560 | target 16 561 | value 3 562 | ] 563 | edge 564 | [ 565 | source 21 566 | target 17 567 | value 3 568 | ] 569 | edge 570 | [ 571 | source 21 572 | target 18 573 | value 3 574 | ] 575 | edge 576 | [ 577 | source 21 578 | target 19 579 | value 3 580 | ] 581 | edge 582 | [ 583 | source 21 584 | target 20 585 | value 5 586 | ] 587 | edge 588 | [ 589 | source 22 590 | target 16 591 | value 3 592 | ] 593 | edge 594 | [ 595 | source 22 596 | target 17 597 | value 3 598 | ] 599 | edge 600 | [ 601 | source 22 602 | target 18 603 | value 3 604 | ] 605 | edge 606 | [ 607 | source 22 608 | target 19 609 | value 3 610 | ] 611 | edge 612 | [ 613 | source 22 614 | target 20 615 | value 4 616 | ] 617 | edge 618 | [ 619 | source 22 620 | target 21 621 | value 4 622 | ] 623 | edge 624 | [ 625 | source 23 626 | target 16 627 | value 3 628 | ] 629 | edge 630 | [ 631 | source 23 632 | target 17 633 | value 3 634 | ] 635 | edge 636 | [ 637 | source 23 638 | target 18 639 | value 3 640 | ] 641 | edge 642 | [ 643 | source 23 644 | target 19 645 | value 3 646 | ] 647 | edge 648 | [ 649 | source 23 650 | target 20 651 | value 4 652 | ] 653 | edge 654 | [ 655 | source 23 656 | target 21 657 | value 4 658 | ] 659 | edge 660 | [ 661 | source 23 662 | target 22 663 | value 4 664 | ] 665 | edge 666 | [ 667 | source 23 668 | target 12 669 | value 2 670 | ] 671 | edge 672 | [ 673 | source 23 674 | target 11 675 | value 9 676 | ] 677 | edge 678 | [ 679 | source 24 680 | target 23 681 | value 2 682 | ] 683 | edge 684 | [ 685 | source 24 686 | target 11 687 | value 7 688 | ] 689 | edge 690 | [ 691 | source 25 692 | target 24 693 | value 13 694 | ] 695 | edge 696 | [ 697 | source 25 698 | target 23 699 | value 1 700 | ] 701 | edge 702 | [ 703 | source 25 704 | target 11 705 | value 12 706 | ] 707 | edge 708 | [ 709 | source 26 710 | target 24 711 | value 4 712 | ] 713 | edge 714 | [ 715 | source 26 716 | target 11 717 | value 31 718 | ] 719 | edge 720 | [ 721 | source 26 722 | target 16 723 | value 1 724 | ] 725 | edge 726 | [ 727 | source 26 728 | target 25 729 | value 1 730 | ] 731 | edge 732 | [ 733 | source 27 734 | target 11 735 | value 17 736 | ] 737 | edge 738 | [ 739 | source 27 740 | target 23 741 | value 5 742 | ] 743 | edge 744 | [ 745 | source 27 746 | target 25 747 | value 5 748 | ] 749 | edge 750 | [ 751 | source 27 752 | target 24 753 | value 1 754 | ] 755 | edge 756 | [ 757 | source 27 758 | target 26 759 | value 1 760 | ] 761 | edge 762 | [ 763 | source 28 764 | target 11 765 | value 8 766 | ] 767 | edge 768 | [ 769 | source 28 770 | target 27 771 | value 1 772 | ] 773 | edge 774 | [ 775 | source 29 776 | target 23 777 | value 1 778 | ] 779 | edge 780 | [ 781 | source 29 782 | target 27 783 | value 1 784 | ] 785 | edge 786 | [ 787 | source 29 788 | target 11 789 | value 2 790 | ] 791 | edge 792 | [ 793 | source 30 794 | target 23 795 | value 1 796 | ] 797 | edge 798 | [ 799 | source 31 800 | target 30 801 | value 2 802 | ] 803 | edge 804 | [ 805 | source 31 806 | target 11 807 | value 3 808 | ] 809 | edge 810 | [ 811 | source 31 812 | target 23 813 | value 2 814 | ] 815 | edge 816 | [ 817 | source 31 818 | target 27 819 | value 1 820 | ] 821 | edge 822 | [ 823 | source 32 824 | target 11 825 | value 1 826 | ] 827 | edge 828 | [ 829 | source 33 830 | target 11 831 | value 2 832 | ] 833 | edge 834 | [ 835 | source 33 836 | target 27 837 | value 1 838 | ] 839 | edge 840 | [ 841 | source 34 842 | target 11 843 | value 3 844 | ] 845 | edge 846 | [ 847 | source 34 848 | target 29 849 | value 2 850 | ] 851 | edge 852 | [ 853 | source 35 854 | target 11 855 | value 3 856 | ] 857 | edge 858 | [ 859 | source 35 860 | target 34 861 | value 3 862 | ] 863 | edge 864 | [ 865 | source 35 866 | target 29 867 | value 2 868 | ] 869 | edge 870 | [ 871 | source 36 872 | target 34 873 | value 2 874 | ] 875 | edge 876 | [ 877 | source 36 878 | target 35 879 | value 2 880 | ] 881 | edge 882 | [ 883 | source 36 884 | target 11 885 | value 2 886 | ] 887 | edge 888 | [ 889 | source 36 890 | target 29 891 | value 1 892 | ] 893 | edge 894 | [ 895 | source 37 896 | target 34 897 | value 2 898 | ] 899 | edge 900 | [ 901 | source 37 902 | target 35 903 | value 2 904 | ] 905 | edge 906 | [ 907 | source 37 908 | target 36 909 | value 2 910 | ] 911 | edge 912 | [ 913 | source 37 914 | target 11 915 | value 2 916 | ] 917 | edge 918 | [ 919 | source 37 920 | target 29 921 | value 1 922 | ] 923 | edge 924 | [ 925 | source 38 926 | target 34 927 | value 2 928 | ] 929 | edge 930 | [ 931 | source 38 932 | target 35 933 | value 2 934 | ] 935 | edge 936 | [ 937 | source 38 938 | target 36 939 | value 2 940 | ] 941 | edge 942 | [ 943 | source 38 944 | target 37 945 | value 2 946 | ] 947 | edge 948 | [ 949 | source 38 950 | target 11 951 | value 2 952 | ] 953 | edge 954 | [ 955 | source 38 956 | target 29 957 | value 1 958 | ] 959 | edge 960 | [ 961 | source 39 962 | target 25 963 | value 1 964 | ] 965 | edge 966 | [ 967 | source 40 968 | target 25 969 | value 1 970 | ] 971 | edge 972 | [ 973 | source 41 974 | target 24 975 | value 2 976 | ] 977 | edge 978 | [ 979 | source 41 980 | target 25 981 | value 3 982 | ] 983 | edge 984 | [ 985 | source 42 986 | target 41 987 | value 2 988 | ] 989 | edge 990 | [ 991 | source 42 992 | target 25 993 | value 2 994 | ] 995 | edge 996 | [ 997 | source 42 998 | target 24 999 | value 1 1000 | ] 1001 | edge 1002 | [ 1003 | source 43 1004 | target 11 1005 | value 3 1006 | ] 1007 | edge 1008 | [ 1009 | source 43 1010 | target 26 1011 | value 1 1012 | ] 1013 | edge 1014 | [ 1015 | source 43 1016 | target 27 1017 | value 1 1018 | ] 1019 | edge 1020 | [ 1021 | source 44 1022 | target 28 1023 | value 3 1024 | ] 1025 | edge 1026 | [ 1027 | source 44 1028 | target 11 1029 | value 1 1030 | ] 1031 | edge 1032 | [ 1033 | source 45 1034 | target 28 1035 | value 2 1036 | ] 1037 | edge 1038 | [ 1039 | source 47 1040 | target 46 1041 | value 1 1042 | ] 1043 | edge 1044 | [ 1045 | source 48 1046 | target 47 1047 | value 2 1048 | ] 1049 | edge 1050 | [ 1051 | source 48 1052 | target 25 1053 | value 1 1054 | ] 1055 | edge 1056 | [ 1057 | source 48 1058 | target 27 1059 | value 1 1060 | ] 1061 | edge 1062 | [ 1063 | source 48 1064 | target 11 1065 | value 1 1066 | ] 1067 | edge 1068 | [ 1069 | source 49 1070 | target 26 1071 | value 3 1072 | ] 1073 | edge 1074 | [ 1075 | source 49 1076 | target 11 1077 | value 2 1078 | ] 1079 | edge 1080 | [ 1081 | source 50 1082 | target 49 1083 | value 1 1084 | ] 1085 | edge 1086 | [ 1087 | source 50 1088 | target 24 1089 | value 1 1090 | ] 1091 | edge 1092 | [ 1093 | source 51 1094 | target 49 1095 | value 9 1096 | ] 1097 | edge 1098 | [ 1099 | source 51 1100 | target 26 1101 | value 2 1102 | ] 1103 | edge 1104 | [ 1105 | source 51 1106 | target 11 1107 | value 2 1108 | ] 1109 | edge 1110 | [ 1111 | source 52 1112 | target 51 1113 | value 1 1114 | ] 1115 | edge 1116 | [ 1117 | source 52 1118 | target 39 1119 | value 1 1120 | ] 1121 | edge 1122 | [ 1123 | source 53 1124 | target 51 1125 | value 1 1126 | ] 1127 | edge 1128 | [ 1129 | source 54 1130 | target 51 1131 | value 2 1132 | ] 1133 | edge 1134 | [ 1135 | source 54 1136 | target 49 1137 | value 1 1138 | ] 1139 | edge 1140 | [ 1141 | source 54 1142 | target 26 1143 | value 1 1144 | ] 1145 | edge 1146 | [ 1147 | source 55 1148 | target 51 1149 | value 6 1150 | ] 1151 | edge 1152 | [ 1153 | source 55 1154 | target 49 1155 | value 12 1156 | ] 1157 | edge 1158 | [ 1159 | source 55 1160 | target 39 1161 | value 1 1162 | ] 1163 | edge 1164 | [ 1165 | source 55 1166 | target 54 1167 | value 1 1168 | ] 1169 | edge 1170 | [ 1171 | source 55 1172 | target 26 1173 | value 21 1174 | ] 1175 | edge 1176 | [ 1177 | source 55 1178 | target 11 1179 | value 19 1180 | ] 1181 | edge 1182 | [ 1183 | source 55 1184 | target 16 1185 | value 1 1186 | ] 1187 | edge 1188 | [ 1189 | source 55 1190 | target 25 1191 | value 2 1192 | ] 1193 | edge 1194 | [ 1195 | source 55 1196 | target 41 1197 | value 5 1198 | ] 1199 | edge 1200 | [ 1201 | source 55 1202 | target 48 1203 | value 4 1204 | ] 1205 | edge 1206 | [ 1207 | source 56 1208 | target 49 1209 | value 1 1210 | ] 1211 | edge 1212 | [ 1213 | source 56 1214 | target 55 1215 | value 1 1216 | ] 1217 | edge 1218 | [ 1219 | source 57 1220 | target 55 1221 | value 1 1222 | ] 1223 | edge 1224 | [ 1225 | source 57 1226 | target 41 1227 | value 1 1228 | ] 1229 | edge 1230 | [ 1231 | source 57 1232 | target 48 1233 | value 1 1234 | ] 1235 | edge 1236 | [ 1237 | source 58 1238 | target 55 1239 | value 7 1240 | ] 1241 | edge 1242 | [ 1243 | source 58 1244 | target 48 1245 | value 7 1246 | ] 1247 | edge 1248 | [ 1249 | source 58 1250 | target 27 1251 | value 6 1252 | ] 1253 | edge 1254 | [ 1255 | source 58 1256 | target 57 1257 | value 1 1258 | ] 1259 | edge 1260 | [ 1261 | source 58 1262 | target 11 1263 | value 4 1264 | ] 1265 | edge 1266 | [ 1267 | source 59 1268 | target 58 1269 | value 15 1270 | ] 1271 | edge 1272 | [ 1273 | source 59 1274 | target 55 1275 | value 5 1276 | ] 1277 | edge 1278 | [ 1279 | source 59 1280 | target 48 1281 | value 6 1282 | ] 1283 | edge 1284 | [ 1285 | source 59 1286 | target 57 1287 | value 2 1288 | ] 1289 | edge 1290 | [ 1291 | source 60 1292 | target 48 1293 | value 1 1294 | ] 1295 | edge 1296 | [ 1297 | source 60 1298 | target 58 1299 | value 4 1300 | ] 1301 | edge 1302 | [ 1303 | source 60 1304 | target 59 1305 | value 2 1306 | ] 1307 | edge 1308 | [ 1309 | source 61 1310 | target 48 1311 | value 2 1312 | ] 1313 | edge 1314 | [ 1315 | source 61 1316 | target 58 1317 | value 6 1318 | ] 1319 | edge 1320 | [ 1321 | source 61 1322 | target 60 1323 | value 2 1324 | ] 1325 | edge 1326 | [ 1327 | source 61 1328 | target 59 1329 | value 5 1330 | ] 1331 | edge 1332 | [ 1333 | source 61 1334 | target 57 1335 | value 1 1336 | ] 1337 | edge 1338 | [ 1339 | source 61 1340 | target 55 1341 | value 1 1342 | ] 1343 | edge 1344 | [ 1345 | source 62 1346 | target 55 1347 | value 9 1348 | ] 1349 | edge 1350 | [ 1351 | source 62 1352 | target 58 1353 | value 17 1354 | ] 1355 | edge 1356 | [ 1357 | source 62 1358 | target 59 1359 | value 13 1360 | ] 1361 | edge 1362 | [ 1363 | source 62 1364 | target 48 1365 | value 7 1366 | ] 1367 | edge 1368 | [ 1369 | source 62 1370 | target 57 1371 | value 2 1372 | ] 1373 | edge 1374 | [ 1375 | source 62 1376 | target 41 1377 | value 1 1378 | ] 1379 | edge 1380 | [ 1381 | source 62 1382 | target 61 1383 | value 6 1384 | ] 1385 | edge 1386 | [ 1387 | source 62 1388 | target 60 1389 | value 3 1390 | ] 1391 | edge 1392 | [ 1393 | source 63 1394 | target 59 1395 | value 5 1396 | ] 1397 | edge 1398 | [ 1399 | source 63 1400 | target 48 1401 | value 5 1402 | ] 1403 | edge 1404 | [ 1405 | source 63 1406 | target 62 1407 | value 6 1408 | ] 1409 | edge 1410 | [ 1411 | source 63 1412 | target 57 1413 | value 2 1414 | ] 1415 | edge 1416 | [ 1417 | source 63 1418 | target 58 1419 | value 4 1420 | ] 1421 | edge 1422 | [ 1423 | source 63 1424 | target 61 1425 | value 3 1426 | ] 1427 | edge 1428 | [ 1429 | source 63 1430 | target 60 1431 | value 2 1432 | ] 1433 | edge 1434 | [ 1435 | source 63 1436 | target 55 1437 | value 1 1438 | ] 1439 | edge 1440 | [ 1441 | source 64 1442 | target 55 1443 | value 5 1444 | ] 1445 | edge 1446 | [ 1447 | source 64 1448 | target 62 1449 | value 12 1450 | ] 1451 | edge 1452 | [ 1453 | source 64 1454 | target 48 1455 | value 5 1456 | ] 1457 | edge 1458 | [ 1459 | source 64 1460 | target 63 1461 | value 4 1462 | ] 1463 | edge 1464 | [ 1465 | source 64 1466 | target 58 1467 | value 10 1468 | ] 1469 | edge 1470 | [ 1471 | source 64 1472 | target 61 1473 | value 6 1474 | ] 1475 | edge 1476 | [ 1477 | source 64 1478 | target 60 1479 | value 2 1480 | ] 1481 | edge 1482 | [ 1483 | source 64 1484 | target 59 1485 | value 9 1486 | ] 1487 | edge 1488 | [ 1489 | source 64 1490 | target 57 1491 | value 1 1492 | ] 1493 | edge 1494 | [ 1495 | source 64 1496 | target 11 1497 | value 1 1498 | ] 1499 | edge 1500 | [ 1501 | source 65 1502 | target 63 1503 | value 5 1504 | ] 1505 | edge 1506 | [ 1507 | source 65 1508 | target 64 1509 | value 7 1510 | ] 1511 | edge 1512 | [ 1513 | source 65 1514 | target 48 1515 | value 3 1516 | ] 1517 | edge 1518 | [ 1519 | source 65 1520 | target 62 1521 | value 5 1522 | ] 1523 | edge 1524 | [ 1525 | source 65 1526 | target 58 1527 | value 5 1528 | ] 1529 | edge 1530 | [ 1531 | source 65 1532 | target 61 1533 | value 5 1534 | ] 1535 | edge 1536 | [ 1537 | source 65 1538 | target 60 1539 | value 2 1540 | ] 1541 | edge 1542 | [ 1543 | source 65 1544 | target 59 1545 | value 5 1546 | ] 1547 | edge 1548 | [ 1549 | source 65 1550 | target 57 1551 | value 1 1552 | ] 1553 | edge 1554 | [ 1555 | source 65 1556 | target 55 1557 | value 2 1558 | ] 1559 | edge 1560 | [ 1561 | source 66 1562 | target 64 1563 | value 3 1564 | ] 1565 | edge 1566 | [ 1567 | source 66 1568 | target 58 1569 | value 3 1570 | ] 1571 | edge 1572 | [ 1573 | source 66 1574 | target 59 1575 | value 1 1576 | ] 1577 | edge 1578 | [ 1579 | source 66 1580 | target 62 1581 | value 2 1582 | ] 1583 | edge 1584 | [ 1585 | source 66 1586 | target 65 1587 | value 2 1588 | ] 1589 | edge 1590 | [ 1591 | source 66 1592 | target 48 1593 | value 1 1594 | ] 1595 | edge 1596 | [ 1597 | source 66 1598 | target 63 1599 | value 1 1600 | ] 1601 | edge 1602 | [ 1603 | source 66 1604 | target 61 1605 | value 1 1606 | ] 1607 | edge 1608 | [ 1609 | source 66 1610 | target 60 1611 | value 1 1612 | ] 1613 | edge 1614 | [ 1615 | source 67 1616 | target 57 1617 | value 3 1618 | ] 1619 | edge 1620 | [ 1621 | source 68 1622 | target 25 1623 | value 5 1624 | ] 1625 | edge 1626 | [ 1627 | source 68 1628 | target 11 1629 | value 1 1630 | ] 1631 | edge 1632 | [ 1633 | source 68 1634 | target 24 1635 | value 1 1636 | ] 1637 | edge 1638 | [ 1639 | source 68 1640 | target 27 1641 | value 1 1642 | ] 1643 | edge 1644 | [ 1645 | source 68 1646 | target 48 1647 | value 1 1648 | ] 1649 | edge 1650 | [ 1651 | source 68 1652 | target 41 1653 | value 1 1654 | ] 1655 | edge 1656 | [ 1657 | source 69 1658 | target 25 1659 | value 6 1660 | ] 1661 | edge 1662 | [ 1663 | source 69 1664 | target 68 1665 | value 6 1666 | ] 1667 | edge 1668 | [ 1669 | source 69 1670 | target 11 1671 | value 1 1672 | ] 1673 | edge 1674 | [ 1675 | source 69 1676 | target 24 1677 | value 1 1678 | ] 1679 | edge 1680 | [ 1681 | source 69 1682 | target 27 1683 | value 2 1684 | ] 1685 | edge 1686 | [ 1687 | source 69 1688 | target 48 1689 | value 1 1690 | ] 1691 | edge 1692 | [ 1693 | source 69 1694 | target 41 1695 | value 1 1696 | ] 1697 | edge 1698 | [ 1699 | source 70 1700 | target 25 1701 | value 4 1702 | ] 1703 | edge 1704 | [ 1705 | source 70 1706 | target 69 1707 | value 4 1708 | ] 1709 | edge 1710 | [ 1711 | source 70 1712 | target 68 1713 | value 4 1714 | ] 1715 | edge 1716 | [ 1717 | source 70 1718 | target 11 1719 | value 1 1720 | ] 1721 | edge 1722 | [ 1723 | source 70 1724 | target 24 1725 | value 1 1726 | ] 1727 | edge 1728 | [ 1729 | source 70 1730 | target 27 1731 | value 1 1732 | ] 1733 | edge 1734 | [ 1735 | source 70 1736 | target 41 1737 | value 1 1738 | ] 1739 | edge 1740 | [ 1741 | source 70 1742 | target 58 1743 | value 1 1744 | ] 1745 | edge 1746 | [ 1747 | source 71 1748 | target 27 1749 | value 1 1750 | ] 1751 | edge 1752 | [ 1753 | source 71 1754 | target 69 1755 | value 2 1756 | ] 1757 | edge 1758 | [ 1759 | source 71 1760 | target 68 1761 | value 2 1762 | ] 1763 | edge 1764 | [ 1765 | source 71 1766 | target 70 1767 | value 2 1768 | ] 1769 | edge 1770 | [ 1771 | source 71 1772 | target 11 1773 | value 1 1774 | ] 1775 | edge 1776 | [ 1777 | source 71 1778 | target 48 1779 | value 1 1780 | ] 1781 | edge 1782 | [ 1783 | source 71 1784 | target 41 1785 | value 1 1786 | ] 1787 | edge 1788 | [ 1789 | source 71 1790 | target 25 1791 | value 1 1792 | ] 1793 | edge 1794 | [ 1795 | source 72 1796 | target 26 1797 | value 2 1798 | ] 1799 | edge 1800 | [ 1801 | source 72 1802 | target 27 1803 | value 1 1804 | ] 1805 | edge 1806 | [ 1807 | source 72 1808 | target 11 1809 | value 1 1810 | ] 1811 | edge 1812 | [ 1813 | source 73 1814 | target 48 1815 | value 2 1816 | ] 1817 | edge 1818 | [ 1819 | source 74 1820 | target 48 1821 | value 2 1822 | ] 1823 | edge 1824 | [ 1825 | source 74 1826 | target 73 1827 | value 3 1828 | ] 1829 | edge 1830 | [ 1831 | source 75 1832 | target 69 1833 | value 3 1834 | ] 1835 | edge 1836 | [ 1837 | source 75 1838 | target 68 1839 | value 3 1840 | ] 1841 | edge 1842 | [ 1843 | source 75 1844 | target 25 1845 | value 3 1846 | ] 1847 | edge 1848 | [ 1849 | source 75 1850 | target 48 1851 | value 1 1852 | ] 1853 | edge 1854 | [ 1855 | source 75 1856 | target 41 1857 | value 1 1858 | ] 1859 | edge 1860 | [ 1861 | source 75 1862 | target 70 1863 | value 1 1864 | ] 1865 | edge 1866 | [ 1867 | source 75 1868 | target 71 1869 | value 1 1870 | ] 1871 | edge 1872 | [ 1873 | source 76 1874 | target 64 1875 | value 1 1876 | ] 1877 | edge 1878 | [ 1879 | source 76 1880 | target 65 1881 | value 1 1882 | ] 1883 | edge 1884 | [ 1885 | source 76 1886 | target 66 1887 | value 1 1888 | ] 1889 | edge 1890 | [ 1891 | source 76 1892 | target 63 1893 | value 1 1894 | ] 1895 | edge 1896 | [ 1897 | source 76 1898 | target 62 1899 | value 1 1900 | ] 1901 | edge 1902 | [ 1903 | source 76 1904 | target 48 1905 | value 1 1906 | ] 1907 | edge 1908 | [ 1909 | source 76 1910 | target 58 1911 | value 1 1912 | ] 1913 | ] 1914 | -------------------------------------------------------------------------------- /maxQkarate.gephi: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sikasjc/CommunityDetection/7d9a4058380c02a6200b4a5dad7fab33b7dfe975/maxQkarate.gephi -------------------------------------------------------------------------------- /maxQlesmiserables.gephi: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sikasjc/CommunityDetection/7d9a4058380c02a6200b4a5dad7fab33b7dfe975/maxQlesmiserables.gephi --------------------------------------------------------------------------------