├── LICENSE ├── README.md ├── datasets ├── dolphins_gt.gml ├── football.gml ├── polblogs.gml └── polbooks.gml ├── dolphin_dwt_ae.py ├── dolphin_dwt_reconstruct.py ├── dwt_customLayer.py ├── football_dwt_ae.py ├── football_dwt_reconstruct.py ├── images ├── Dolphin.jpg ├── Football.jpg ├── Polblogs.jpg ├── Polbooks.jpg └── thismodel.jpg ├── polblogs_dwt_ae.py ├── polblogs_dwt_reconstruct.py ├── polbooks_dwt_ae.py ├── polbooks_dwt_reconstruct.py └── thesis.pdf /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 DHILBER M 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # community-detection-DL 2 | This repository is the implementation of paper : Community Detection in Social Networks using Deep Learning. 3 | This paper was accepted in ICDCIT 2020 conference. [link](https://link.springer.com/chapter/10.1007/978-3-030-36987-3_15) 4 | -------------------------------------------------------------------------------- /datasets/dolphins_gt.gml: -------------------------------------------------------------------------------- 1 | Creator "Mark Newman on Wed Jul 26 15:04:20 2006" 2 | graph 3 | [ 4 | directed 0 5 | node 6 | [ 7 | id 0 8 | label "Beak" 9 | value 1 10 | ] 11 | node 12 | [ 13 | id 1 14 | label "Beescratch" 15 | value 0 16 | ] 17 | node 18 | [ 19 | id 2 20 | label "Bumper" 21 | value 1 22 | ] 23 | node 24 | [ 25 | id 3 26 | label "CCL" 27 | value 2 28 | ] 29 | node 30 | [ 31 | id 4 32 | label "Cross" 33 | value 3 34 | ] 35 | node 36 | [ 37 | id 5 38 | label "DN16" 39 | value 0 40 | ] 41 | node 42 | [ 43 | id 6 44 | label "DN21" 45 | value 0 46 | ] 47 | node 48 | [ 49 | id 7 50 | label "DN63" 51 | value 0 52 | ] 53 | node 54 | [ 55 | id 8 56 | label "Double" 57 | value 2 58 | ] 59 | node 60 | [ 61 | id 9 62 | label "Feather" 63 | value 0 64 | ] 65 | node 66 | [ 67 | id 10 68 | label "Fish" 69 | value 1 70 | ] 71 | node 72 | [ 73 | id 11 74 | label "Five" 75 | value 3 76 | ] 77 | node 78 | [ 79 | id 12 80 | label "Fork" 81 | value 2 82 | ] 83 | node 84 | [ 85 | id 13 86 | label "Gallatin" 87 | value 0 88 | ] 89 | node 90 | [ 91 | id 14 92 | label "Grin" 93 | value 2 94 | ] 95 | node 96 | [ 97 | id 15 98 | label "Haecksel" 99 | value 3 100 | ] 101 | node 102 | [ 103 | id 16 104 | label "Hook" 105 | value 2 106 | ] 107 | node 108 | [ 109 | id 17 110 | label "Jet" 111 | value 0 112 | ] 113 | node 114 | [ 115 | id 18 116 | label "Jonah" 117 | value 3 118 | ] 119 | node 120 | [ 121 | id 19 122 | label "Knit" 123 | value 0 124 | ] 125 | node 126 | [ 127 | id 20 128 | label "Kringel" 129 | value 2 130 | ] 131 | node 132 | [ 133 | id 21 134 | label "MN105" 135 | value 3 136 | ] 137 | node 138 | [ 139 | id 22 140 | label "MN23" 141 | value 0 142 | ] 143 | node 144 | [ 145 | id 23 146 | label "MN60" 147 | value 3 148 | ] 149 | node 150 | [ 151 | id 24 152 | label "MN83" 153 | value 3 154 | ] 155 | node 156 | [ 157 | id 25 158 | label "Mus" 159 | value 0 160 | ] 161 | node 162 | [ 163 | id 26 164 | label "Notch" 165 | value 0 166 | ] 167 | node 168 | [ 169 | id 27 170 | label "Number1" 171 | value 0 172 | ] 173 | node 174 | [ 175 | id 28 176 | label "Oscar" 177 | value 1 178 | ] 179 | node 180 | [ 181 | id 29 182 | label "Patchback" 183 | value 3 184 | ] 185 | node 186 | [ 187 | id 30 188 | label "PL" 189 | value 1 190 | ] 191 | node 192 | [ 193 | id 31 194 | label "Quasi" 195 | value 0 196 | ] 197 | node 198 | [ 199 | id 32 200 | label "Ripplefluke" 201 | value 0 202 | ] 203 | node 204 | [ 205 | id 33 206 | label "Scabs" 207 | value 2 208 | ] 209 | node 210 | [ 211 | id 34 212 | label "Shmuddel" 213 | value 2 214 | ] 215 | node 216 | [ 217 | id 35 218 | label "SMN5" 219 | value 3 220 | ] 221 | node 222 | [ 223 | id 36 224 | label "SN100" 225 | value 2 226 | ] 227 | node 228 | [ 229 | id 37 230 | label "SN4" 231 | value 2 232 | ] 233 | node 234 | [ 235 | id 38 236 | label "SN63" 237 | value 2 238 | ] 239 | node 240 | [ 241 | id 39 242 | label "SN89" 243 | value 2 244 | ] 245 | node 246 | [ 247 | id 40 248 | label "SN9" 249 | value 2 250 | ] 251 | node 252 | [ 253 | id 41 254 | label "SN90" 255 | value 0 256 | ] 257 | node 258 | [ 259 | id 42 260 | label "SN96" 261 | value 1 262 | ] 263 | node 264 | [ 265 | id 43 266 | label "Stripes" 267 | value 2 268 | ] 269 | node 270 | [ 271 | id 44 272 | label "Thumper" 273 | value 2 274 | ] 275 | node 276 | [ 277 | id 45 278 | label "Topless" 279 | value 3 280 | ] 281 | node 282 | [ 283 | id 46 284 | label "TR120" 285 | value 2 286 | ] 287 | node 288 | [ 289 | id 47 290 | label "TR77" 291 | value 1 292 | ] 293 | node 294 | [ 295 | id 48 296 | label "TR82" 297 | value 0 298 | ] 299 | node 300 | [ 301 | id 49 302 | label "TR88" 303 | value 2 304 | ] 305 | node 306 | [ 307 | id 50 308 | label "TR99" 309 | value 2 310 | ] 311 | node 312 | [ 313 | id 51 314 | label "Trigger" 315 | value 3 316 | ] 317 | node 318 | [ 319 | id 52 320 | label "TSN103" 321 | value 2 322 | ] 323 | node 324 | [ 325 | id 53 326 | label "TSN83" 327 | value 2 328 | ] 329 | node 330 | [ 331 | id 54 332 | label "Upbang" 333 | value 0 334 | ] 335 | node 336 | [ 337 | id 55 338 | label "Vau" 339 | value 3 340 | ] 341 | node 342 | [ 343 | id 56 344 | label "Wave" 345 | value 0 346 | ] 347 | node 348 | [ 349 | id 57 350 | label "Web" 351 | value 0 352 | ] 353 | node 354 | [ 355 | id 58 356 | label "Whitetip" 357 | value 2 358 | ] 359 | node 360 | [ 361 | id 59 362 | label "Zap" 363 | value 2 364 | ] 365 | node 366 | [ 367 | id 60 368 | label "Zig" 369 | value 0 370 | ] 371 | node 372 | [ 373 | id 61 374 | label "Zipfel" 375 | value 2 376 | ] 377 | edge 378 | [ 379 | source 8 380 | target 3 381 | ] 382 | edge 383 | [ 384 | source 9 385 | target 5 386 | ] 387 | edge 388 | [ 389 | source 9 390 | target 6 391 | ] 392 | edge 393 | [ 394 | source 10 395 | target 0 396 | ] 397 | edge 398 | [ 399 | source 10 400 | target 2 401 | ] 402 | edge 403 | [ 404 | source 13 405 | target 5 406 | ] 407 | edge 408 | [ 409 | source 13 410 | target 6 411 | ] 412 | edge 413 | [ 414 | source 13 415 | target 9 416 | ] 417 | edge 418 | [ 419 | source 14 420 | target 0 421 | ] 422 | edge 423 | [ 424 | source 14 425 | target 3 426 | ] 427 | edge 428 | [ 429 | source 15 430 | target 0 431 | ] 432 | edge 433 | [ 434 | source 16 435 | target 14 436 | ] 437 | edge 438 | [ 439 | source 17 440 | target 1 441 | ] 442 | edge 443 | [ 444 | source 17 445 | target 6 446 | ] 447 | edge 448 | [ 449 | source 17 450 | target 9 451 | ] 452 | edge 453 | [ 454 | source 17 455 | target 13 456 | ] 457 | edge 458 | [ 459 | source 18 460 | target 15 461 | ] 462 | edge 463 | [ 464 | source 19 465 | target 1 466 | ] 467 | edge 468 | [ 469 | source 19 470 | target 7 471 | ] 472 | edge 473 | [ 474 | source 20 475 | target 8 476 | ] 477 | edge 478 | [ 479 | source 20 480 | target 16 481 | ] 482 | edge 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738 | [ 739 | source 40 740 | target 14 741 | ] 742 | edge 743 | [ 744 | source 40 745 | target 15 746 | ] 747 | edge 748 | [ 749 | source 40 750 | target 33 751 | ] 752 | edge 753 | [ 754 | source 40 755 | target 36 756 | ] 757 | edge 758 | [ 759 | source 40 760 | target 37 761 | ] 762 | edge 763 | [ 764 | source 41 765 | target 1 766 | ] 767 | edge 768 | [ 769 | source 41 770 | target 9 771 | ] 772 | edge 773 | [ 774 | source 41 775 | target 13 776 | ] 777 | edge 778 | [ 779 | source 42 780 | target 0 781 | ] 782 | edge 783 | [ 784 | source 42 785 | target 2 786 | ] 787 | edge 788 | [ 789 | source 42 790 | target 10 791 | ] 792 | edge 793 | [ 794 | source 42 795 | target 30 796 | ] 797 | edge 798 | [ 799 | source 43 800 | target 14 801 | ] 802 | edge 803 | [ 804 | source 43 805 | target 29 806 | ] 807 | edge 808 | [ 809 | source 43 810 | target 33 811 | ] 812 | edge 813 | [ 814 | source 43 815 | target 37 816 | ] 817 | edge 818 | [ 819 | source 43 820 | target 38 821 | ] 822 | edge 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908 | [ 909 | source 47 910 | target 30 911 | ] 912 | edge 913 | [ 914 | source 47 915 | target 42 916 | ] 917 | edge 918 | [ 919 | source 49 920 | target 34 921 | ] 922 | edge 923 | [ 924 | source 49 925 | target 46 926 | ] 927 | edge 928 | [ 929 | source 50 930 | target 14 931 | ] 932 | edge 933 | [ 934 | source 50 935 | target 16 936 | ] 937 | edge 938 | [ 939 | source 50 940 | target 20 941 | ] 942 | edge 943 | [ 944 | source 50 945 | target 33 946 | ] 947 | edge 948 | [ 949 | source 50 950 | target 42 951 | ] 952 | edge 953 | [ 954 | source 50 955 | target 45 956 | ] 957 | edge 958 | [ 959 | source 51 960 | target 4 961 | ] 962 | edge 963 | [ 964 | source 51 965 | target 11 966 | ] 967 | edge 968 | [ 969 | source 51 970 | target 18 971 | ] 972 | edge 973 | [ 974 | source 51 975 | target 21 976 | ] 977 | edge 978 | [ 979 | source 51 980 | target 23 981 | ] 982 | edge 983 | [ 984 | source 51 985 | target 24 986 | ] 987 | edge 988 | [ 989 | source 51 990 | target 29 991 | ] 992 | edge 993 | [ 994 | source 51 995 | target 45 996 | ] 997 | edge 998 | [ 999 | source 51 1000 | target 50 1001 | ] 1002 | edge 1003 | [ 1004 | source 52 1005 | target 14 1006 | ] 1007 | edge 1008 | [ 1009 | source 52 1010 | target 29 1011 | ] 1012 | edge 1013 | [ 1014 | source 52 1015 | target 38 1016 | ] 1017 | edge 1018 | [ 1019 | source 52 1020 | target 40 1021 | ] 1022 | edge 1023 | [ 1024 | source 53 1025 | target 43 1026 | ] 1027 | edge 1028 | [ 1029 | source 54 1030 | target 1 1031 | ] 1032 | edge 1033 | [ 1034 | source 54 1035 | target 6 1036 | ] 1037 | edge 1038 | [ 1039 | source 54 1040 | target 7 1041 | ] 1042 | edge 1043 | [ 1044 | source 54 1045 | target 13 1046 | ] 1047 | edge 1048 | [ 1049 | source 54 1050 | target 19 1051 | ] 1052 | edge 1053 | [ 1054 | source 54 1055 | target 41 1056 | ] 1057 | edge 1058 | [ 1059 | source 55 1060 | target 15 1061 | ] 1062 | edge 1063 | [ 1064 | source 55 1065 | target 51 1066 | ] 1067 | edge 1068 | [ 1069 | source 56 1070 | target 5 1071 | ] 1072 | edge 1073 | [ 1074 | source 56 1075 | target 6 1076 | ] 1077 | edge 1078 | [ 1079 | source 57 1080 | target 5 1081 | ] 1082 | edge 1083 | [ 1084 | source 57 1085 | target 6 1086 | ] 1087 | edge 1088 | [ 1089 | source 57 1090 | target 9 1091 | ] 1092 | edge 1093 | [ 1094 | source 57 1095 | target 13 1096 | ] 1097 | edge 1098 | [ 1099 | source 57 1100 | target 17 1101 | ] 1102 | edge 1103 | [ 1104 | source 57 1105 | target 39 1106 | ] 1107 | edge 1108 | [ 1109 | source 57 1110 | target 41 1111 | ] 1112 | edge 1113 | [ 1114 | source 57 1115 | target 48 1116 | ] 1117 | edge 1118 | [ 1119 | source 57 1120 | target 54 1121 | ] 1122 | edge 1123 | [ 1124 | source 58 1125 | target 38 1126 | ] 1127 | edge 1128 | [ 1129 | source 59 1130 | target 3 1131 | ] 1132 | edge 1133 | [ 1134 | source 59 1135 | target 8 1136 | ] 1137 | edge 1138 | [ 1139 | source 59 1140 | target 15 1141 | ] 1142 | edge 1143 | [ 1144 | source 59 1145 | target 36 1146 | ] 1147 | edge 1148 | [ 1149 | source 59 1150 | target 45 1151 | ] 1152 | edge 1153 | [ 1154 | source 60 1155 | target 32 1156 | ] 1157 | edge 1158 | [ 1159 | source 61 1160 | target 2 1161 | ] 1162 | edge 1163 | [ 1164 | source 61 1165 | target 37 1166 | ] 1167 | edge 1168 | [ 1169 | source 61 1170 | target 53 1171 | ] 1172 | ] 1173 | -------------------------------------------------------------------------------- /datasets/football.gml: -------------------------------------------------------------------------------- 1 | Creator "Mark Newman on Sat Jul 22 05:32:16 2006" 2 | graph 3 | [ 4 | directed 0 5 | node 6 | [ 7 | id 0 8 | label "BrighamYoung" 9 | value 7 10 | ] 11 | node 12 | [ 13 | id 1 14 | label "FloridaState" 15 | value 0 16 | ] 17 | node 18 | [ 19 | id 2 20 | label "Iowa" 21 | value 2 22 | ] 23 | node 24 | [ 25 | id 3 26 | label "KansasState" 27 | value 3 28 | ] 29 | node 30 | [ 31 | id 4 32 | label "NewMexico" 33 | value 7 34 | ] 35 | node 36 | [ 37 | id 5 38 | label "TexasTech" 39 | value 3 40 | ] 41 | node 42 | [ 43 | id 6 44 | label "PennState" 45 | value 2 46 | ] 47 | node 48 | [ 49 | id 7 50 | label "SouthernCalifornia" 51 | value 8 52 | ] 53 | node 54 | [ 55 | id 8 56 | label "ArizonaState" 57 | value 8 58 | ] 59 | node 60 | [ 61 | id 9 62 | label "SanDiegoState" 63 | value 7 64 | ] 65 | node 66 | [ 67 | id 10 68 | label "Baylor" 69 | value 3 70 | ] 71 | node 72 | [ 73 | id 11 74 | label "NorthTexas" 75 | value 10 76 | ] 77 | node 78 | [ 79 | id 12 80 | label "NorthernIllinois" 81 | value 6 82 | ] 83 | node 84 | [ 85 | id 13 86 | label "Northwestern" 87 | value 2 88 | ] 89 | node 90 | [ 91 | id 14 92 | label "WesternMichigan" 93 | value 6 94 | ] 95 | node 96 | [ 97 | id 15 98 | label "Wisconsin" 99 | value 2 100 | ] 101 | node 102 | [ 103 | id 16 104 | label "Wyoming" 105 | value 7 106 | ] 107 | node 108 | [ 109 | id 17 110 | label "Auburn" 111 | value 9 112 | ] 113 | node 114 | [ 115 | id 18 116 | label "Akron" 117 | value 6 118 | ] 119 | node 120 | [ 121 | id 19 122 | label "VirginiaTech" 123 | value 1 124 | ] 125 | node 126 | [ 127 | id 20 128 | label "Alabama" 129 | value 9 130 | ] 131 | node 132 | [ 133 | id 21 134 | label "UCLA" 135 | value 8 136 | ] 137 | node 138 | [ 139 | id 22 140 | label "Arizona" 141 | value 8 142 | ] 143 | node 144 | [ 145 | id 23 146 | label "Utah" 147 | value 7 148 | ] 149 | node 150 | [ 151 | id 24 152 | label "ArkansasState" 153 | value 10 154 | ] 155 | node 156 | [ 157 | id 25 158 | label "NorthCarolinaState" 159 | value 0 160 | ] 161 | node 162 | [ 163 | id 26 164 | label "BallState" 165 | value 6 166 | ] 167 | node 168 | [ 169 | id 27 170 | label "Florida" 171 | value 9 172 | ] 173 | node 174 | [ 175 | id 28 176 | label "BoiseState" 177 | value 11 178 | ] 179 | node 180 | [ 181 | id 29 182 | label "BostonCollege" 183 | value 1 184 | ] 185 | node 186 | [ 187 | id 30 188 | label "WestVirginia" 189 | value 1 190 | ] 191 | node 192 | [ 193 | id 31 194 | label "BowlingGreenState" 195 | value 6 196 | ] 197 | node 198 | [ 199 | id 32 200 | label "Michigan" 201 | value 2 202 | ] 203 | node 204 | [ 205 | id 33 206 | label "Virginia" 207 | value 0 208 | ] 209 | node 210 | [ 211 | id 34 212 | label "Buffalo" 213 | value 6 214 | ] 215 | node 216 | [ 217 | id 35 218 | label "Syracuse" 219 | value 1 220 | ] 221 | node 222 | [ 223 | id 36 224 | label "CentralFlorida" 225 | value 5 226 | ] 227 | node 228 | [ 229 | id 37 230 | label "GeorgiaTech" 231 | value 0 232 | ] 233 | node 234 | [ 235 | id 38 236 | label "CentralMichigan" 237 | value 6 238 | ] 239 | node 240 | [ 241 | id 39 242 | label "Purdue" 243 | value 2 244 | ] 245 | node 246 | [ 247 | id 40 248 | label "Colorado" 249 | value 3 250 | ] 251 | node 252 | [ 253 | id 41 254 | label "ColoradoState" 255 | value 7 256 | ] 257 | node 258 | [ 259 | id 42 260 | label "Connecticut" 261 | value 5 262 | ] 263 | node 264 | [ 265 | id 43 266 | label "EasternMichigan" 267 | value 6 268 | ] 269 | node 270 | [ 271 | id 44 272 | label "EastCarolina" 273 | value 4 274 | ] 275 | node 276 | [ 277 | id 45 278 | label "Duke" 279 | value 0 280 | ] 281 | node 282 | [ 283 | id 46 284 | label "FresnoState" 285 | value 11 286 | ] 287 | node 288 | [ 289 | id 47 290 | label "OhioState" 291 | value 2 292 | ] 293 | node 294 | [ 295 | id 48 296 | label "Houston" 297 | value 4 298 | ] 299 | node 300 | [ 301 | id 49 302 | label "Rice" 303 | value 11 304 | ] 305 | node 306 | [ 307 | id 50 308 | label "Idaho" 309 | value 10 310 | ] 311 | node 312 | [ 313 | id 51 314 | label "Washington" 315 | value 8 316 | ] 317 | node 318 | [ 319 | id 52 320 | label "Kansas" 321 | value 3 322 | ] 323 | node 324 | [ 325 | id 53 326 | label "SouthernMethodist" 327 | value 11 328 | ] 329 | node 330 | [ 331 | id 54 332 | label "Kent" 333 | value 6 334 | ] 335 | node 336 | [ 337 | id 55 338 | label "Pittsburgh" 339 | value 1 340 | ] 341 | node 342 | [ 343 | id 56 344 | label "Kentucky" 345 | value 9 346 | ] 347 | node 348 | [ 349 | id 57 350 | label "Louisville" 351 | value 4 352 | ] 353 | node 354 | [ 355 | id 58 356 | label "LouisianaTech" 357 | value 11 358 | ] 359 | node 360 | [ 361 | id 59 362 | label "LouisianaMonroe" 363 | value 10 364 | ] 365 | node 366 | [ 367 | id 60 368 | label "Minnesota" 369 | value 2 370 | ] 371 | node 372 | [ 373 | id 61 374 | label "MiamiOhio" 375 | value 6 376 | ] 377 | node 378 | [ 379 | id 62 380 | label "Vanderbilt" 381 | value 9 382 | ] 383 | node 384 | [ 385 | id 63 386 | label "MiddleTennesseeState" 387 | value 10 388 | ] 389 | node 390 | [ 391 | id 64 392 | label "Illinois" 393 | value 2 394 | ] 395 | node 396 | [ 397 | id 65 398 | label "MississippiState" 399 | value 9 400 | ] 401 | node 402 | [ 403 | id 66 404 | label "Memphis" 405 | value 4 406 | ] 407 | node 408 | [ 409 | id 67 410 | label "Nevada" 411 | value 11 412 | ] 413 | node 414 | [ 415 | id 68 416 | label "Oregon" 417 | value 8 418 | ] 419 | node 420 | [ 421 | id 69 422 | label "NewMexicoState" 423 | value 10 424 | ] 425 | node 426 | [ 427 | id 70 428 | label "SouthCarolina" 429 | value 9 430 | ] 431 | node 432 | [ 433 | id 71 434 | label "Ohio" 435 | value 6 436 | ] 437 | node 438 | [ 439 | id 72 440 | label "IowaState" 441 | value 3 442 | ] 443 | node 444 | [ 445 | id 73 446 | label "SanJoseState" 447 | value 11 448 | ] 449 | node 450 | [ 451 | id 74 452 | label "Nebraska" 453 | value 3 454 | ] 455 | node 456 | [ 457 | id 75 458 | label "SouthernMississippi" 459 | value 4 460 | ] 461 | node 462 | [ 463 | id 76 464 | label "Tennessee" 465 | value 9 466 | ] 467 | node 468 | [ 469 | id 77 470 | label "Stanford" 471 | value 8 472 | ] 473 | node 474 | [ 475 | id 78 476 | label "WashingtonState" 477 | value 8 478 | ] 479 | node 480 | [ 481 | id 79 482 | label "Temple" 483 | value 1 484 | ] 485 | node 486 | [ 487 | id 80 488 | label "Navy" 489 | value 5 490 | ] 491 | node 492 | [ 493 | id 81 494 | label "TexasA&M" 495 | value 3 496 | ] 497 | node 498 | [ 499 | id 82 500 | label "NotreDame" 501 | value 5 502 | ] 503 | node 504 | [ 505 | id 83 506 | label "TexasElPaso" 507 | value 11 508 | ] 509 | node 510 | [ 511 | id 84 512 | label "Oklahoma" 513 | value 3 514 | ] 515 | node 516 | [ 517 | id 85 518 | label "Toledo" 519 | value 6 520 | ] 521 | node 522 | [ 523 | id 86 524 | label "Tulane" 525 | value 4 526 | ] 527 | node 528 | [ 529 | id 87 530 | label "Mississippi" 531 | value 9 532 | ] 533 | node 534 | [ 535 | id 88 536 | label "Tulsa" 537 | value 11 538 | ] 539 | node 540 | [ 541 | id 89 542 | label "NorthCarolina" 543 | value 0 544 | ] 545 | node 546 | [ 547 | id 90 548 | label "UtahState" 549 | value 5 550 | ] 551 | node 552 | [ 553 | id 91 554 | label "Army" 555 | value 4 556 | ] 557 | node 558 | [ 559 | id 92 560 | label "Cincinnati" 561 | value 4 562 | ] 563 | node 564 | [ 565 | id 93 566 | label "AirForce" 567 | value 7 568 | ] 569 | node 570 | [ 571 | id 94 572 | label "Rutgers" 573 | value 1 574 | ] 575 | node 576 | [ 577 | id 95 578 | label "Georgia" 579 | value 9 580 | ] 581 | node 582 | [ 583 | id 96 584 | label "LouisianaState" 585 | value 9 586 | ] 587 | node 588 | [ 589 | id 97 590 | label "LouisianaLafayette" 591 | value 10 592 | ] 593 | node 594 | [ 595 | id 98 596 | label "Texas" 597 | value 3 598 | ] 599 | node 600 | [ 601 | id 99 602 | label "Marshall" 603 | value 6 604 | ] 605 | node 606 | [ 607 | id 100 608 | label "MichiganState" 609 | value 2 610 | ] 611 | node 612 | [ 613 | id 101 614 | label "MiamiFlorida" 615 | value 1 616 | ] 617 | node 618 | [ 619 | id 102 620 | label "Missouri" 621 | value 3 622 | ] 623 | node 624 | [ 625 | id 103 626 | label "Clemson" 627 | value 0 628 | ] 629 | node 630 | [ 631 | id 104 632 | label "NevadaLasVegas" 633 | value 7 634 | ] 635 | node 636 | [ 637 | id 105 638 | label "WakeForest" 639 | value 0 640 | ] 641 | node 642 | [ 643 | id 106 644 | label "Indiana" 645 | value 2 646 | ] 647 | node 648 | [ 649 | id 107 650 | label "OklahomaState" 651 | value 3 652 | ] 653 | node 654 | [ 655 | id 108 656 | label "OregonState" 657 | value 8 658 | ] 659 | node 660 | [ 661 | id 109 662 | label "Maryland" 663 | value 0 664 | ] 665 | node 666 | [ 667 | id 110 668 | label "TexasChristian" 669 | value 4 670 | ] 671 | node 672 | [ 673 | id 111 674 | label "California" 675 | value 8 676 | ] 677 | node 678 | [ 679 | id 112 680 | label "AlabamaBirmingham" 681 | value 4 682 | ] 683 | node 684 | [ 685 | id 113 686 | label "Arkansas" 687 | value 9 688 | ] 689 | node 690 | [ 691 | id 114 692 | label "Hawaii" 693 | value 11 694 | ] 695 | edge 696 | [ 697 | source 1 698 | target 0 699 | ] 700 | edge 701 | [ 702 | source 3 703 | target 2 704 | ] 705 | edge 706 | [ 707 | source 4 708 | target 0 709 | ] 710 | edge 711 | [ 712 | source 5 713 | target 4 714 | ] 715 | edge 716 | [ 717 | source 5 718 | target 3 719 | ] 720 | edge 721 | [ 722 | source 6 723 | target 2 724 | ] 725 | edge 726 | [ 727 | source 7 728 | target 6 729 | ] 730 | edge 731 | [ 732 | source 8 733 | target 7 734 | ] 735 | edge 736 | [ 737 | source 9 738 | target 8 739 | ] 740 | edge 741 | [ 742 | source 9 743 | target 0 744 | ] 745 | edge 746 | [ 747 | source 9 748 | target 4 749 | ] 750 | edge 751 | [ 752 | source 10 753 | target 5 754 | ] 755 | edge 756 | [ 757 | source 11 758 | target 10 759 | ] 760 | edge 761 | [ 762 | source 11 763 | target 5 764 | ] 765 | edge 766 | [ 767 | source 11 768 | target 3 769 | ] 770 | edge 771 | [ 772 | source 13 773 | target 12 774 | ] 775 | edge 776 | [ 777 | source 13 778 | target 2 779 | ] 780 | edge 781 | [ 782 | source 14 783 | target 2 784 | ] 785 | edge 786 | [ 787 | source 14 788 | target 12 789 | ] 790 | edge 791 | [ 792 | source 15 793 | target 14 794 | ] 795 | edge 796 | [ 797 | source 15 798 | target 13 799 | ] 800 | edge 801 | [ 802 | source 15 803 | target 2 804 | ] 805 | edge 806 | [ 807 | source 16 808 | target 4 809 | ] 810 | edge 811 | [ 812 | source 16 813 | target 9 814 | ] 815 | edge 816 | [ 817 | source 16 818 | target 0 819 | ] 820 | edge 821 | [ 822 | source 17 823 | target 16 824 | ] 825 | edge 826 | [ 827 | source 17 828 | target 12 829 | ] 830 | edge 831 | [ 832 | source 18 833 | target 12 834 | ] 835 | edge 836 | [ 837 | source 19 838 | target 18 839 | ] 840 | edge 841 | [ 842 | source 20 843 | target 17 844 | ] 845 | edge 846 | [ 847 | source 21 848 | target 20 849 | ] 850 | edge 851 | [ 852 | source 21 853 | target 8 854 | ] 855 | edge 856 | [ 857 | source 21 858 | target 7 859 | ] 860 | edge 861 | [ 862 | source 22 863 | target 9 864 | ] 865 | edge 866 | [ 867 | source 22 868 | target 7 869 | ] 870 | edge 871 | [ 872 | source 22 873 | target 21 874 | ] 875 | edge 876 | [ 877 | source 22 878 | target 8 879 | ] 880 | edge 881 | [ 882 | source 23 883 | target 22 884 | ] 885 | edge 886 | [ 887 | source 23 888 | target 9 889 | ] 890 | edge 891 | [ 892 | source 23 893 | target 4 894 | ] 895 | edge 896 | [ 897 | source 23 898 | target 16 899 | ] 900 | edge 901 | [ 902 | source 23 903 | target 0 904 | ] 905 | edge 906 | [ 907 | source 24 908 | target 11 909 | ] 910 | edge 911 | [ 912 | source 25 913 | target 24 914 | ] 915 | edge 916 | [ 917 | source 25 918 | target 1 919 | ] 920 | edge 921 | [ 922 | source 26 923 | target 3 924 | ] 925 | edge 926 | [ 927 | source 26 928 | target 12 929 | ] 930 | edge 931 | [ 932 | source 26 933 | target 14 934 | ] 935 | edge 936 | [ 937 | source 27 938 | target 26 939 | ] 940 | edge 941 | [ 942 | source 27 943 | target 17 944 | ] 945 | edge 946 | [ 947 | source 27 948 | target 1 949 | ] 950 | edge 951 | [ 952 | source 28 953 | target 4 954 | ] 955 | edge 956 | [ 957 | source 28 958 | target 11 959 | ] 960 | edge 961 | [ 962 | source 28 963 | target 24 964 | ] 965 | edge 966 | [ 967 | source 29 968 | target 19 969 | ] 970 | edge 971 | [ 972 | source 30 973 | target 29 974 | ] 975 | edge 976 | [ 977 | source 30 978 | target 19 979 | ] 980 | edge 981 | [ 982 | source 31 983 | target 18 984 | ] 985 | edge 986 | [ 987 | source 32 988 | target 31 989 | ] 990 | edge 991 | [ 992 | source 32 993 | target 21 994 | ] 995 | edge 996 | [ 997 | source 32 998 | target 15 999 | ] 1000 | edge 1001 | [ 1002 | source 32 1003 | target 13 1004 | ] 1005 | edge 1006 | [ 1007 | source 32 1008 | target 6 1009 | ] 1010 | edge 1011 | [ 1012 | source 33 1013 | target 0 1014 | ] 1015 | edge 1016 | [ 1017 | source 33 1018 | target 1 1019 | ] 1020 | edge 1021 | [ 1022 | source 33 1023 | target 25 1024 | ] 1025 | edge 1026 | [ 1027 | source 33 1028 | target 19 1029 | ] 1030 | edge 1031 | [ 1032 | source 34 1033 | target 31 1034 | ] 1035 | edge 1036 | [ 1037 | source 34 1038 | target 26 1039 | ] 1040 | edge 1041 | [ 1042 | source 34 1043 | target 12 1044 | ] 1045 | edge 1046 | [ 1047 | source 34 1048 | target 18 1049 | ] 1050 | edge 1051 | [ 1052 | source 35 1053 | target 34 1054 | ] 1055 | edge 1056 | [ 1057 | source 35 1058 | target 0 1059 | ] 1060 | edge 1061 | [ 1062 | source 35 1063 | target 29 1064 | ] 1065 | edge 1066 | [ 1067 | source 35 1068 | target 19 1069 | ] 1070 | edge 1071 | [ 1072 | source 35 1073 | target 30 1074 | ] 1075 | edge 1076 | [ 1077 | source 36 1078 | target 18 1079 | ] 1080 | edge 1081 | [ 1082 | source 36 1083 | target 12 1084 | ] 1085 | edge 1086 | [ 1087 | source 36 1088 | target 20 1089 | ] 1090 | edge 1091 | [ 1092 | source 36 1093 | target 19 1094 | ] 1095 | edge 1096 | [ 1097 | source 37 1098 | target 36 1099 | ] 1100 | edge 1101 | [ 1102 | source 37 1103 | target 1 1104 | ] 1105 | edge 1106 | [ 1107 | source 37 1108 | target 25 1109 | ] 1110 | edge 1111 | [ 1112 | source 37 1113 | target 33 1114 | ] 1115 | edge 1116 | [ 1117 | source 38 1118 | target 18 1119 | ] 1120 | edge 1121 | [ 1122 | source 38 1123 | target 16 1124 | ] 1125 | edge 1126 | [ 1127 | source 38 1128 | target 28 1129 | ] 1130 | edge 1131 | [ 1132 | source 38 1133 | target 26 1134 | ] 1135 | edge 1136 | [ 1137 | source 38 1138 | target 14 1139 | ] 1140 | edge 1141 | [ 1142 | source 38 1143 | target 12 1144 | ] 1145 | edge 1146 | [ 1147 | source 39 1148 | target 38 1149 | ] 1150 | edge 1151 | [ 1152 | source 39 1153 | target 6 1154 | ] 1155 | edge 1156 | [ 1157 | source 39 1158 | target 32 1159 | ] 1160 | edge 1161 | [ 1162 | source 39 1163 | target 13 1164 | ] 1165 | edge 1166 | [ 1167 | source 39 1168 | target 15 1169 | ] 1170 | edge 1171 | [ 1172 | source 40 1173 | target 7 1174 | ] 1175 | edge 1176 | [ 1177 | source 40 1178 | target 3 1179 | ] 1180 | edge 1181 | [ 1182 | source 41 1183 | target 40 1184 | ] 1185 | edge 1186 | [ 1187 | source 41 1188 | target 8 1189 | ] 1190 | edge 1191 | [ 1192 | source 41 1193 | target 4 1194 | ] 1195 | edge 1196 | [ 1197 | source 41 1198 | target 23 1199 | ] 1200 | edge 1201 | [ 1202 | source 41 1203 | target 9 1204 | ] 1205 | edge 1206 | [ 1207 | source 41 1208 | target 0 1209 | ] 1210 | edge 1211 | [ 1212 | source 41 1213 | target 16 1214 | ] 1215 | edge 1216 | [ 1217 | source 42 1218 | target 34 1219 | ] 1220 | edge 1221 | [ 1222 | source 42 1223 | target 29 1224 | ] 1225 | edge 1226 | [ 1227 | source 42 1228 | target 18 1229 | ] 1230 | edge 1231 | [ 1232 | source 42 1233 | target 26 1234 | ] 1235 | edge 1236 | [ 1237 | source 43 1238 | target 42 1239 | ] 1240 | edge 1241 | [ 1242 | source 43 1243 | target 36 1244 | ] 1245 | edge 1246 | [ 1247 | source 43 1248 | target 26 1249 | ] 1250 | edge 1251 | [ 1252 | source 43 1253 | target 31 1254 | ] 1255 | edge 1256 | [ 1257 | source 43 1258 | target 38 1259 | ] 1260 | edge 1261 | [ 1262 | source 43 1263 | target 12 1264 | ] 1265 | edge 1266 | [ 1267 | source 43 1268 | target 14 1269 | ] 1270 | edge 1271 | [ 1272 | source 44 1273 | target 19 1274 | ] 1275 | edge 1276 | [ 1277 | source 44 1278 | target 35 1279 | ] 1280 | edge 1281 | [ 1282 | source 44 1283 | target 30 1284 | ] 1285 | edge 1286 | [ 1287 | source 45 1288 | target 44 1289 | ] 1290 | edge 1291 | [ 1292 | source 45 1293 | target 13 1294 | ] 1295 | edge 1296 | [ 1297 | source 45 1298 | target 33 1299 | ] 1300 | edge 1301 | [ 1302 | source 45 1303 | target 1 1304 | ] 1305 | edge 1306 | [ 1307 | source 45 1308 | target 37 1309 | ] 1310 | edge 1311 | [ 1312 | source 45 1313 | target 25 1314 | ] 1315 | edge 1316 | [ 1317 | source 46 1318 | target 21 1319 | ] 1320 | edge 1321 | [ 1322 | source 47 1323 | target 46 1324 | ] 1325 | edge 1326 | [ 1327 | source 47 1328 | target 22 1329 | ] 1330 | edge 1331 | [ 1332 | source 47 1333 | target 6 1334 | ] 1335 | edge 1336 | [ 1337 | source 47 1338 | target 15 1339 | ] 1340 | edge 1341 | [ 1342 | source 47 1343 | target 2 1344 | ] 1345 | edge 1346 | [ 1347 | source 47 1348 | target 39 1349 | ] 1350 | edge 1351 | [ 1352 | source 47 1353 | target 32 1354 | ] 1355 | edge 1356 | [ 1357 | source 48 1358 | target 44 1359 | ] 1360 | edge 1361 | [ 1362 | source 49 1363 | target 48 1364 | ] 1365 | edge 1366 | [ 1367 | source 49 1368 | target 32 1369 | ] 1370 | edge 1371 | [ 1372 | source 49 1373 | target 46 1374 | ] 1375 | edge 1376 | [ 1377 | source 50 1378 | target 30 1379 | ] 1380 | edge 1381 | [ 1382 | source 50 1383 | target 24 1384 | ] 1385 | edge 1386 | [ 1387 | source 50 1388 | target 11 1389 | ] 1390 | edge 1391 | [ 1392 | source 50 1393 | target 28 1394 | ] 1395 | edge 1396 | [ 1397 | source 51 1398 | target 50 1399 | ] 1400 | edge 1401 | [ 1402 | source 51 1403 | target 40 1404 | ] 1405 | edge 1406 | [ 1407 | source 51 1408 | target 8 1409 | ] 1410 | edge 1411 | [ 1412 | source 51 1413 | target 22 1414 | ] 1415 | edge 1416 | [ 1417 | source 51 1418 | target 21 1419 | ] 1420 | edge 1421 | [ 1422 | source 52 1423 | target 3 1424 | ] 1425 | edge 1426 | [ 1427 | source 52 1428 | target 40 1429 | ] 1430 | edge 1431 | [ 1432 | source 52 1433 | target 5 1434 | ] 1435 | edge 1436 | [ 1437 | source 53 1438 | target 52 1439 | ] 1440 | edge 1441 | [ 1442 | source 53 1443 | target 25 1444 | ] 1445 | edge 1446 | [ 1447 | source 53 1448 | target 48 1449 | ] 1450 | edge 1451 | [ 1452 | source 53 1453 | target 49 1454 | ] 1455 | edge 1456 | [ 1457 | source 53 1458 | target 46 1459 | ] 1460 | edge 1461 | [ 1462 | source 54 1463 | target 39 1464 | ] 1465 | edge 1466 | [ 1467 | source 54 1468 | target 31 1469 | ] 1470 | edge 1471 | [ 1472 | source 54 1473 | target 38 1474 | ] 1475 | edge 1476 | [ 1477 | source 54 1478 | target 14 1479 | ] 1480 | edge 1481 | [ 1482 | source 54 1483 | target 34 1484 | ] 1485 | edge 1486 | [ 1487 | source 54 1488 | target 18 1489 | ] 1490 | edge 1491 | [ 1492 | source 55 1493 | target 54 1494 | ] 1495 | edge 1496 | [ 1497 | source 55 1498 | target 31 1499 | ] 1500 | edge 1501 | [ 1502 | source 55 1503 | target 6 1504 | ] 1505 | edge 1506 | [ 1507 | source 55 1508 | target 35 1509 | ] 1510 | edge 1511 | [ 1512 | source 55 1513 | target 29 1514 | ] 1515 | edge 1516 | [ 1517 | source 55 1518 | target 19 1519 | ] 1520 | edge 1521 | [ 1522 | source 55 1523 | target 30 1524 | ] 1525 | edge 1526 | [ 1527 | source 56 1528 | target 27 1529 | ] 1530 | edge 1531 | [ 1532 | source 57 1533 | target 56 1534 | ] 1535 | edge 1536 | [ 1537 | source 57 1538 | target 1 1539 | ] 1540 | edge 1541 | [ 1542 | source 57 1543 | target 42 1544 | ] 1545 | edge 1546 | [ 1547 | source 57 1548 | target 44 1549 | ] 1550 | edge 1551 | [ 1552 | source 57 1553 | target 48 1554 | ] 1555 | edge 1556 | [ 1557 | source 58 1558 | target 3 1559 | ] 1560 | edge 1561 | [ 1562 | source 58 1563 | target 6 1564 | ] 1565 | edge 1566 | [ 1567 | source 58 1568 | target 17 1569 | ] 1570 | edge 1571 | [ 1572 | source 58 1573 | target 36 1574 | ] 1575 | edge 1576 | [ 1577 | source 59 1578 | target 36 1579 | ] 1580 | edge 1581 | [ 1582 | source 59 1583 | target 58 1584 | ] 1585 | edge 1586 | [ 1587 | source 60 1588 | target 59 1589 | ] 1590 | edge 1591 | [ 1592 | source 60 1593 | target 10 1594 | ] 1595 | edge 1596 | [ 1597 | source 60 1598 | target 39 1599 | ] 1600 | edge 1601 | [ 1602 | source 60 1603 | target 6 1604 | ] 1605 | edge 1606 | [ 1607 | source 60 1608 | target 47 1609 | ] 1610 | edge 1611 | [ 1612 | source 60 1613 | target 13 1614 | ] 1615 | edge 1616 | [ 1617 | source 60 1618 | target 15 1619 | ] 1620 | edge 1621 | [ 1622 | source 60 1623 | target 2 1624 | ] 1625 | edge 1626 | [ 1627 | source 61 1628 | target 43 1629 | ] 1630 | edge 1631 | [ 1632 | source 61 1633 | target 47 1634 | ] 1635 | edge 1636 | [ 1637 | source 61 1638 | target 54 1639 | ] 1640 | edge 1641 | [ 1642 | source 61 1643 | target 18 1644 | ] 1645 | edge 1646 | [ 1647 | source 61 1648 | target 26 1649 | ] 1650 | edge 1651 | [ 1652 | source 61 1653 | target 31 1654 | ] 1655 | edge 1656 | [ 1657 | source 61 1658 | target 34 1659 | ] 1660 | edge 1661 | [ 1662 | source 62 1663 | target 61 1664 | ] 1665 | edge 1666 | [ 1667 | source 62 1668 | target 20 1669 | ] 1670 | edge 1671 | [ 1672 | source 62 1673 | target 45 1674 | ] 1675 | edge 1676 | [ 1677 | source 62 1678 | target 17 1679 | ] 1680 | edge 1681 | [ 1682 | source 62 1683 | target 27 1684 | ] 1685 | edge 1686 | [ 1687 | source 62 1688 | target 56 1689 | ] 1690 | edge 1691 | [ 1692 | source 63 1693 | target 27 1694 | ] 1695 | edge 1696 | [ 1697 | source 63 1698 | target 58 1699 | ] 1700 | edge 1701 | [ 1702 | source 63 1703 | target 59 1704 | ] 1705 | edge 1706 | [ 1707 | source 63 1708 | target 42 1709 | ] 1710 | edge 1711 | [ 1712 | source 64 1713 | target 63 1714 | ] 1715 | edge 1716 | [ 1717 | source 64 1718 | target 9 1719 | ] 1720 | edge 1721 | [ 1722 | source 64 1723 | target 32 1724 | ] 1725 | edge 1726 | [ 1727 | source 64 1728 | target 60 1729 | ] 1730 | edge 1731 | [ 1732 | source 64 1733 | target 2 1734 | ] 1735 | edge 1736 | [ 1737 | source 64 1738 | target 6 1739 | ] 1740 | edge 1741 | [ 1742 | source 64 1743 | target 47 1744 | ] 1745 | edge 1746 | [ 1747 | source 64 1748 | target 13 1749 | ] 1750 | edge 1751 | [ 1752 | source 65 1753 | target 0 1754 | ] 1755 | edge 1756 | [ 1757 | source 65 1758 | target 27 1759 | ] 1760 | edge 1761 | [ 1762 | source 65 1763 | target 17 1764 | ] 1765 | edge 1766 | [ 1767 | source 65 1768 | target 63 1769 | ] 1770 | edge 1771 | [ 1772 | source 65 1773 | target 56 1774 | ] 1775 | edge 1776 | [ 1777 | source 65 1778 | target 20 1779 | ] 1780 | edge 1781 | [ 1782 | source 66 1783 | target 65 1784 | ] 1785 | edge 1786 | [ 1787 | source 66 1788 | target 59 1789 | ] 1790 | edge 1791 | [ 1792 | source 66 1793 | target 24 1794 | ] 1795 | edge 1796 | [ 1797 | source 66 1798 | target 44 1799 | ] 1800 | edge 1801 | [ 1802 | source 66 1803 | target 48 1804 | ] 1805 | edge 1806 | [ 1807 | source 67 1808 | target 16 1809 | ] 1810 | edge 1811 | [ 1812 | source 67 1813 | target 41 1814 | ] 1815 | edge 1816 | [ 1817 | source 67 1818 | target 46 1819 | ] 1820 | edge 1821 | [ 1822 | source 67 1823 | target 53 1824 | ] 1825 | edge 1826 | [ 1827 | source 67 1828 | target 49 1829 | ] 1830 | edge 1831 | [ 1832 | source 68 1833 | target 67 1834 | ] 1835 | edge 1836 | [ 1837 | source 68 1838 | target 15 1839 | ] 1840 | edge 1841 | [ 1842 | source 68 1843 | target 50 1844 | ] 1845 | edge 1846 | [ 1847 | source 68 1848 | target 21 1849 | ] 1850 | edge 1851 | [ 1852 | source 68 1853 | target 51 1854 | ] 1855 | edge 1856 | [ 1857 | source 68 1858 | target 7 1859 | ] 1860 | edge 1861 | [ 1862 | source 68 1863 | target 22 1864 | ] 1865 | edge 1866 | [ 1867 | source 68 1868 | target 8 1869 | ] 1870 | edge 1871 | [ 1872 | source 69 1873 | target 4 1874 | ] 1875 | edge 1876 | [ 1877 | source 69 1878 | target 24 1879 | ] 1880 | edge 1881 | [ 1882 | source 69 1883 | target 28 1884 | ] 1885 | edge 1886 | [ 1887 | source 69 1888 | target 50 1889 | ] 1890 | edge 1891 | [ 1892 | source 69 1893 | target 11 1894 | ] 1895 | edge 1896 | [ 1897 | source 70 1898 | target 69 1899 | ] 1900 | edge 1901 | [ 1902 | source 70 1903 | target 43 1904 | ] 1905 | edge 1906 | [ 1907 | source 70 1908 | target 65 1909 | ] 1910 | edge 1911 | [ 1912 | source 70 1913 | target 20 1914 | ] 1915 | edge 1916 | [ 1917 | source 70 1918 | target 56 1919 | ] 1920 | edge 1921 | [ 1922 | source 70 1923 | target 62 1924 | ] 1925 | edge 1926 | [ 1927 | source 70 1928 | target 27 1929 | ] 1930 | edge 1931 | [ 1932 | source 71 1933 | target 60 1934 | ] 1935 | edge 1936 | [ 1937 | source 71 1938 | target 18 1939 | ] 1940 | edge 1941 | [ 1942 | source 71 1943 | target 14 1944 | ] 1945 | edge 1946 | [ 1947 | source 71 1948 | target 34 1949 | ] 1950 | edge 1951 | [ 1952 | source 71 1953 | target 54 1954 | ] 1955 | edge 1956 | [ 1957 | source 71 1958 | target 38 1959 | ] 1960 | edge 1961 | [ 1962 | source 71 1963 | target 61 1964 | ] 1965 | edge 1966 | [ 1967 | source 71 1968 | target 31 1969 | ] 1970 | edge 1971 | [ 1972 | source 72 1973 | target 71 1974 | ] 1975 | edge 1976 | [ 1977 | source 72 1978 | target 2 1979 | ] 1980 | edge 1981 | [ 1982 | source 72 1983 | target 10 1984 | ] 1985 | edge 1986 | [ 1987 | source 72 1988 | target 3 1989 | ] 1990 | edge 1991 | [ 1992 | source 72 1993 | target 40 1994 | ] 1995 | edge 1996 | [ 1997 | source 72 1998 | target 52 1999 | ] 2000 | edge 2001 | [ 2002 | source 73 2003 | target 7 2004 | ] 2005 | edge 2006 | [ 2007 | source 73 2008 | target 49 2009 | ] 2010 | edge 2011 | [ 2012 | source 73 2013 | target 53 2014 | ] 2015 | edge 2016 | [ 2017 | source 73 2018 | target 67 2019 | ] 2020 | edge 2021 | [ 2022 | source 73 2023 | target 46 2024 | ] 2025 | edge 2026 | [ 2027 | source 74 2028 | target 73 2029 | ] 2030 | edge 2031 | [ 2032 | source 74 2033 | target 2 2034 | ] 2035 | edge 2036 | [ 2037 | source 74 2038 | target 72 2039 | ] 2040 | edge 2041 | [ 2042 | source 74 2043 | target 5 2044 | ] 2045 | edge 2046 | [ 2047 | source 74 2048 | target 10 2049 | ] 2050 | edge 2051 | [ 2052 | source 74 2053 | target 52 2054 | ] 2055 | edge 2056 | [ 2057 | source 74 2058 | target 3 2059 | ] 2060 | edge 2061 | [ 2062 | source 74 2063 | target 40 2064 | ] 2065 | edge 2066 | [ 2067 | source 75 2068 | target 20 2069 | ] 2070 | edge 2071 | [ 2072 | source 75 2073 | target 66 2074 | ] 2075 | edge 2076 | [ 2077 | source 75 2078 | target 48 2079 | ] 2080 | edge 2081 | [ 2082 | source 75 2083 | target 57 2084 | ] 2085 | edge 2086 | [ 2087 | source 75 2088 | target 44 2089 | ] 2090 | edge 2091 | [ 2092 | source 76 2093 | target 75 2094 | ] 2095 | edge 2096 | [ 2097 | source 76 2098 | target 27 2099 | ] 2100 | edge 2101 | [ 2102 | source 76 2103 | target 59 2104 | ] 2105 | edge 2106 | [ 2107 | source 76 2108 | target 20 2109 | ] 2110 | edge 2111 | [ 2112 | source 76 2113 | target 70 2114 | ] 2115 | edge 2116 | [ 2117 | source 76 2118 | target 66 2119 | ] 2120 | edge 2121 | [ 2122 | source 76 2123 | target 56 2124 | ] 2125 | edge 2126 | [ 2127 | source 76 2128 | target 62 2129 | ] 2130 | edge 2131 | [ 2132 | source 77 2133 | target 73 2134 | ] 2135 | edge 2136 | [ 2137 | source 77 2138 | target 22 2139 | ] 2140 | edge 2141 | [ 2142 | source 77 2143 | target 7 2144 | ] 2145 | edge 2146 | [ 2147 | source 77 2148 | target 51 2149 | ] 2150 | edge 2151 | [ 2152 | source 77 2153 | target 21 2154 | ] 2155 | edge 2156 | [ 2157 | source 77 2158 | target 8 2159 | ] 2160 | edge 2161 | [ 2162 | source 78 2163 | target 77 2164 | ] 2165 | edge 2166 | [ 2167 | source 78 2168 | target 23 2169 | ] 2170 | edge 2171 | [ 2172 | source 78 2173 | target 50 2174 | ] 2175 | edge 2176 | [ 2177 | source 78 2178 | target 28 2179 | ] 2180 | edge 2181 | [ 2182 | source 78 2183 | target 22 2184 | ] 2185 | edge 2186 | [ 2187 | source 78 2188 | target 8 2189 | ] 2190 | edge 2191 | [ 2192 | source 78 2193 | target 68 2194 | ] 2195 | edge 2196 | [ 2197 | source 78 2198 | target 7 2199 | ] 2200 | edge 2201 | [ 2202 | source 78 2203 | target 51 2204 | ] 2205 | edge 2206 | [ 2207 | source 79 2208 | target 31 2209 | ] 2210 | edge 2211 | [ 2212 | source 79 2213 | target 43 2214 | ] 2215 | edge 2216 | [ 2217 | source 79 2218 | target 30 2219 | ] 2220 | edge 2221 | [ 2222 | source 79 2223 | target 19 2224 | ] 2225 | edge 2226 | [ 2227 | source 79 2228 | target 29 2229 | ] 2230 | edge 2231 | [ 2232 | source 79 2233 | target 35 2234 | ] 2235 | edge 2236 | [ 2237 | source 79 2238 | target 55 2239 | ] 2240 | edge 2241 | [ 2242 | source 80 2243 | target 79 2244 | ] 2245 | edge 2246 | [ 2247 | source 80 2248 | target 37 2249 | ] 2250 | edge 2251 | [ 2252 | source 80 2253 | target 29 2254 | ] 2255 | edge 2256 | [ 2257 | source 81 2258 | target 16 2259 | ] 2260 | edge 2261 | [ 2262 | source 81 2263 | target 5 2264 | ] 2265 | edge 2266 | [ 2267 | source 81 2268 | target 40 2269 | ] 2270 | edge 2271 | [ 2272 | source 81 2273 | target 10 2274 | ] 2275 | edge 2276 | [ 2277 | source 81 2278 | target 72 2279 | ] 2280 | edge 2281 | [ 2282 | source 81 2283 | target 3 2284 | ] 2285 | edge 2286 | [ 2287 | source 82 2288 | target 81 2289 | ] 2290 | edge 2291 | [ 2292 | source 82 2293 | target 74 2294 | ] 2295 | edge 2296 | [ 2297 | source 82 2298 | target 39 2299 | ] 2300 | edge 2301 | [ 2302 | source 82 2303 | target 77 2304 | ] 2305 | edge 2306 | [ 2307 | source 82 2308 | target 80 2309 | ] 2310 | edge 2311 | [ 2312 | source 82 2313 | target 30 2314 | ] 2315 | edge 2316 | [ 2317 | source 82 2318 | target 29 2319 | ] 2320 | edge 2321 | [ 2322 | source 82 2323 | target 7 2324 | ] 2325 | edge 2326 | [ 2327 | source 83 2328 | target 53 2329 | ] 2330 | edge 2331 | [ 2332 | source 83 2333 | target 81 2334 | ] 2335 | edge 2336 | [ 2337 | source 83 2338 | target 69 2339 | ] 2340 | edge 2341 | [ 2342 | source 83 2343 | target 73 2344 | ] 2345 | edge 2346 | [ 2347 | source 83 2348 | target 46 2349 | ] 2350 | edge 2351 | [ 2352 | source 83 2353 | target 67 2354 | ] 2355 | edge 2356 | [ 2357 | source 83 2358 | target 49 2359 | ] 2360 | edge 2361 | [ 2362 | source 84 2363 | target 83 2364 | ] 2365 | edge 2366 | [ 2367 | source 84 2368 | target 24 2369 | ] 2370 | edge 2371 | [ 2372 | source 84 2373 | target 49 2374 | ] 2375 | edge 2376 | [ 2377 | source 84 2378 | target 52 2379 | ] 2380 | edge 2381 | [ 2382 | source 84 2383 | target 3 2384 | ] 2385 | edge 2386 | [ 2387 | source 84 2388 | target 74 2389 | ] 2390 | edge 2391 | [ 2392 | source 84 2393 | target 10 2394 | ] 2395 | edge 2396 | [ 2397 | source 84 2398 | target 81 2399 | ] 2400 | edge 2401 | [ 2402 | source 84 2403 | target 5 2404 | ] 2405 | edge 2406 | [ 2407 | source 85 2408 | target 6 2409 | ] 2410 | edge 2411 | [ 2412 | source 85 2413 | target 14 2414 | ] 2415 | edge 2416 | [ 2417 | source 85 2418 | target 38 2419 | ] 2420 | edge 2421 | [ 2422 | source 85 2423 | target 43 2424 | ] 2425 | edge 2426 | [ 2427 | source 85 2428 | target 80 2429 | ] 2430 | edge 2431 | [ 2432 | source 85 2433 | target 12 2434 | ] 2435 | edge 2436 | [ 2437 | source 85 2438 | target 26 2439 | ] 2440 | edge 2441 | [ 2442 | source 85 2443 | target 31 2444 | ] 2445 | edge 2446 | [ 2447 | source 86 2448 | target 44 2449 | ] 2450 | edge 2451 | [ 2452 | source 86 2453 | target 53 2454 | ] 2455 | edge 2456 | [ 2457 | source 86 2458 | target 75 2459 | ] 2460 | edge 2461 | [ 2462 | source 86 2463 | target 57 2464 | ] 2465 | edge 2466 | [ 2467 | source 86 2468 | target 48 2469 | ] 2470 | edge 2471 | [ 2472 | source 86 2473 | target 80 2474 | ] 2475 | edge 2476 | [ 2477 | source 86 2478 | target 66 2479 | ] 2480 | edge 2481 | [ 2482 | source 87 2483 | target 86 2484 | ] 2485 | edge 2486 | [ 2487 | source 87 2488 | target 17 2489 | ] 2490 | edge 2491 | [ 2492 | source 87 2493 | target 62 2494 | ] 2495 | edge 2496 | [ 2497 | source 87 2498 | target 56 2499 | ] 2500 | edge 2501 | [ 2502 | source 87 2503 | target 24 2504 | ] 2505 | edge 2506 | [ 2507 | source 87 2508 | target 20 2509 | ] 2510 | edge 2511 | [ 2512 | source 87 2513 | target 65 2514 | ] 2515 | edge 2516 | [ 2517 | source 88 2518 | target 49 2519 | ] 2520 | edge 2521 | [ 2522 | source 88 2523 | target 58 2524 | ] 2525 | edge 2526 | [ 2527 | source 88 2528 | target 83 2529 | ] 2530 | edge 2531 | [ 2532 | source 88 2533 | target 69 2534 | ] 2535 | edge 2536 | [ 2537 | source 88 2538 | target 46 2539 | ] 2540 | edge 2541 | [ 2542 | source 88 2543 | target 53 2544 | ] 2545 | edge 2546 | [ 2547 | source 88 2548 | target 73 2549 | ] 2550 | edge 2551 | [ 2552 | source 88 2553 | target 67 2554 | ] 2555 | edge 2556 | [ 2557 | source 89 2558 | target 88 2559 | ] 2560 | edge 2561 | [ 2562 | source 89 2563 | target 1 2564 | ] 2565 | edge 2566 | [ 2567 | source 89 2568 | target 37 2569 | ] 2570 | edge 2571 | [ 2572 | source 89 2573 | target 25 2574 | ] 2575 | edge 2576 | [ 2577 | source 89 2578 | target 33 2579 | ] 2580 | edge 2581 | [ 2582 | source 89 2583 | target 55 2584 | ] 2585 | edge 2586 | [ 2587 | source 89 2588 | target 45 2589 | ] 2590 | edge 2591 | [ 2592 | source 90 2593 | target 5 2594 | ] 2595 | edge 2596 | [ 2597 | source 90 2598 | target 8 2599 | ] 2600 | edge 2601 | [ 2602 | source 90 2603 | target 23 2604 | ] 2605 | edge 2606 | [ 2607 | source 90 2608 | target 0 2609 | ] 2610 | edge 2611 | [ 2612 | source 90 2613 | target 11 2614 | ] 2615 | edge 2616 | [ 2617 | source 90 2618 | target 50 2619 | ] 2620 | edge 2621 | [ 2622 | source 90 2623 | target 24 2624 | ] 2625 | edge 2626 | [ 2627 | source 90 2628 | target 69 2629 | ] 2630 | edge 2631 | [ 2632 | source 90 2633 | target 28 2634 | ] 2635 | edge 2636 | [ 2637 | source 91 2638 | target 29 2639 | ] 2640 | edge 2641 | [ 2642 | source 91 2643 | target 48 2644 | ] 2645 | edge 2646 | [ 2647 | source 91 2648 | target 66 2649 | ] 2650 | edge 2651 | [ 2652 | source 91 2653 | target 69 2654 | ] 2655 | edge 2656 | [ 2657 | source 91 2658 | target 44 2659 | ] 2660 | edge 2661 | [ 2662 | source 91 2663 | target 86 2664 | ] 2665 | edge 2666 | [ 2667 | source 91 2668 | target 57 2669 | ] 2670 | edge 2671 | [ 2672 | source 91 2673 | target 80 2674 | ] 2675 | edge 2676 | [ 2677 | source 92 2678 | target 91 2679 | ] 2680 | edge 2681 | [ 2682 | source 92 2683 | target 35 2684 | ] 2685 | edge 2686 | [ 2687 | source 92 2688 | target 15 2689 | ] 2690 | edge 2691 | [ 2692 | source 92 2693 | target 86 2694 | ] 2695 | edge 2696 | [ 2697 | source 92 2698 | target 48 2699 | ] 2700 | edge 2701 | [ 2702 | source 92 2703 | target 57 2704 | ] 2705 | edge 2706 | [ 2707 | source 92 2708 | target 61 2709 | ] 2710 | edge 2711 | [ 2712 | source 92 2713 | target 66 2714 | ] 2715 | edge 2716 | [ 2717 | source 92 2718 | target 75 2719 | ] 2720 | edge 2721 | [ 2722 | source 93 2723 | target 0 2724 | ] 2725 | edge 2726 | [ 2727 | source 93 2728 | target 23 2729 | ] 2730 | edge 2731 | [ 2732 | source 93 2733 | target 80 2734 | ] 2735 | edge 2736 | [ 2737 | source 93 2738 | target 16 2739 | ] 2740 | edge 2741 | [ 2742 | source 93 2743 | target 4 2744 | ] 2745 | edge 2746 | [ 2747 | source 93 2748 | target 82 2749 | ] 2750 | edge 2751 | [ 2752 | source 93 2753 | target 91 2754 | ] 2755 | edge 2756 | [ 2757 | source 93 2758 | target 41 2759 | ] 2760 | edge 2761 | [ 2762 | source 93 2763 | target 9 2764 | ] 2765 | edge 2766 | [ 2767 | source 94 2768 | target 34 2769 | ] 2770 | edge 2771 | [ 2772 | source 94 2773 | target 19 2774 | ] 2775 | edge 2776 | [ 2777 | source 94 2778 | target 55 2779 | ] 2780 | edge 2781 | [ 2782 | source 94 2783 | target 79 2784 | ] 2785 | edge 2786 | [ 2787 | source 94 2788 | target 80 2789 | ] 2790 | edge 2791 | [ 2792 | source 94 2793 | target 29 2794 | ] 2795 | edge 2796 | [ 2797 | source 94 2798 | target 30 2799 | ] 2800 | edge 2801 | [ 2802 | source 94 2803 | target 82 2804 | ] 2805 | edge 2806 | [ 2807 | source 94 2808 | target 35 2809 | ] 2810 | edge 2811 | [ 2812 | source 95 2813 | target 70 2814 | ] 2815 | edge 2816 | [ 2817 | source 95 2818 | target 69 2819 | ] 2820 | edge 2821 | [ 2822 | source 95 2823 | target 76 2824 | ] 2825 | edge 2826 | [ 2827 | source 95 2828 | target 62 2829 | ] 2830 | edge 2831 | [ 2832 | source 95 2833 | target 56 2834 | ] 2835 | edge 2836 | [ 2837 | source 95 2838 | target 27 2839 | ] 2840 | edge 2841 | [ 2842 | source 95 2843 | target 17 2844 | ] 2845 | edge 2846 | [ 2847 | source 95 2848 | target 87 2849 | ] 2850 | edge 2851 | [ 2852 | source 95 2853 | target 37 2854 | ] 2855 | edge 2856 | [ 2857 | source 96 2858 | target 48 2859 | ] 2860 | edge 2861 | [ 2862 | source 96 2863 | target 17 2864 | ] 2865 | edge 2866 | [ 2867 | source 96 2868 | target 76 2869 | ] 2870 | edge 2871 | [ 2872 | source 96 2873 | target 27 2874 | ] 2875 | edge 2876 | [ 2877 | source 96 2878 | target 56 2879 | ] 2880 | edge 2881 | [ 2882 | source 96 2883 | target 65 2884 | ] 2885 | edge 2886 | [ 2887 | source 96 2888 | target 20 2889 | ] 2890 | edge 2891 | [ 2892 | source 96 2893 | target 87 2894 | ] 2895 | edge 2896 | [ 2897 | source 97 2898 | target 5 2899 | ] 2900 | edge 2901 | [ 2902 | source 97 2903 | target 86 2904 | ] 2905 | edge 2906 | [ 2907 | source 97 2908 | target 58 2909 | ] 2910 | edge 2911 | [ 2912 | source 97 2913 | target 11 2914 | ] 2915 | edge 2916 | [ 2917 | source 97 2918 | target 59 2919 | ] 2920 | edge 2921 | [ 2922 | source 97 2923 | target 63 2924 | ] 2925 | edge 2926 | [ 2927 | source 98 2928 | target 97 2929 | ] 2930 | edge 2931 | [ 2932 | source 98 2933 | target 77 2934 | ] 2935 | edge 2936 | [ 2937 | source 98 2938 | target 48 2939 | ] 2940 | edge 2941 | [ 2942 | source 98 2943 | target 84 2944 | ] 2945 | edge 2946 | [ 2947 | source 98 2948 | target 40 2949 | ] 2950 | edge 2951 | [ 2952 | source 98 2953 | target 10 2954 | ] 2955 | edge 2956 | [ 2957 | source 98 2958 | target 5 2959 | ] 2960 | edge 2961 | [ 2962 | source 98 2963 | target 52 2964 | ] 2965 | edge 2966 | [ 2967 | source 98 2968 | target 81 2969 | ] 2970 | edge 2971 | [ 2972 | source 99 2973 | target 89 2974 | ] 2975 | edge 2976 | [ 2977 | source 99 2978 | target 34 2979 | ] 2980 | edge 2981 | [ 2982 | source 99 2983 | target 14 2984 | ] 2985 | edge 2986 | [ 2987 | source 99 2988 | target 85 2989 | ] 2990 | edge 2991 | [ 2992 | source 99 2993 | target 54 2994 | ] 2995 | edge 2996 | [ 2997 | source 99 2998 | target 18 2999 | ] 3000 | edge 3001 | [ 3002 | source 99 3003 | target 31 3004 | ] 3005 | edge 3006 | [ 3007 | source 99 3008 | target 61 3009 | ] 3010 | edge 3011 | [ 3012 | source 99 3013 | target 71 3014 | ] 3015 | edge 3016 | [ 3017 | source 100 3018 | target 99 3019 | ] 3020 | edge 3021 | [ 3022 | source 100 3023 | target 82 3024 | ] 3025 | edge 3026 | [ 3027 | source 100 3028 | target 13 3029 | ] 3030 | edge 3031 | [ 3032 | source 100 3033 | target 2 3034 | ] 3035 | edge 3036 | [ 3037 | source 100 3038 | target 15 3039 | ] 3040 | edge 3041 | [ 3042 | source 100 3043 | target 32 3044 | ] 3045 | edge 3046 | [ 3047 | source 100 3048 | target 64 3049 | ] 3050 | edge 3051 | [ 3052 | source 100 3053 | target 47 3054 | ] 3055 | edge 3056 | [ 3057 | source 100 3058 | target 39 3059 | ] 3060 | edge 3061 | [ 3062 | source 100 3063 | target 6 3064 | ] 3065 | edge 3066 | [ 3067 | source 101 3068 | target 51 3069 | ] 3070 | edge 3071 | [ 3072 | source 101 3073 | target 30 3074 | ] 3075 | edge 3076 | [ 3077 | source 101 3078 | target 94 3079 | ] 3080 | edge 3081 | [ 3082 | source 101 3083 | target 1 3084 | ] 3085 | edge 3086 | [ 3087 | source 101 3088 | target 79 3089 | ] 3090 | edge 3091 | [ 3092 | source 101 3093 | target 58 3094 | ] 3095 | edge 3096 | [ 3097 | source 101 3098 | target 19 3099 | ] 3100 | edge 3101 | [ 3102 | source 101 3103 | target 55 3104 | ] 3105 | edge 3106 | [ 3107 | source 101 3108 | target 35 3109 | ] 3110 | edge 3111 | [ 3112 | source 101 3113 | target 29 3114 | ] 3115 | edge 3116 | [ 3117 | source 102 3118 | target 100 3119 | ] 3120 | edge 3121 | [ 3122 | source 102 3123 | target 74 3124 | ] 3125 | edge 3126 | [ 3127 | source 102 3128 | target 52 3129 | ] 3130 | edge 3131 | [ 3132 | source 102 3133 | target 98 3134 | ] 3135 | edge 3136 | [ 3137 | source 102 3138 | target 72 3139 | ] 3140 | edge 3141 | [ 3142 | source 102 3143 | target 40 3144 | ] 3145 | edge 3146 | [ 3147 | source 102 3148 | target 10 3149 | ] 3150 | edge 3151 | [ 3152 | source 102 3153 | target 3 3154 | ] 3155 | edge 3156 | [ 3157 | source 103 3158 | target 102 3159 | ] 3160 | edge 3161 | [ 3162 | source 103 3163 | target 33 3164 | ] 3165 | edge 3166 | [ 3167 | source 103 3168 | target 45 3169 | ] 3170 | edge 3171 | [ 3172 | source 103 3173 | target 25 3174 | ] 3175 | edge 3176 | [ 3177 | source 103 3178 | target 89 3179 | ] 3180 | edge 3181 | [ 3182 | source 103 3183 | target 37 3184 | ] 3185 | edge 3186 | [ 3187 | source 103 3188 | target 1 3189 | ] 3190 | edge 3191 | [ 3192 | source 103 3193 | target 70 3194 | ] 3195 | edge 3196 | [ 3197 | source 104 3198 | target 72 3199 | ] 3200 | edge 3201 | [ 3202 | source 104 3203 | target 11 3204 | ] 3205 | edge 3206 | [ 3207 | source 104 3208 | target 0 3209 | ] 3210 | edge 3211 | [ 3212 | source 104 3213 | target 93 3214 | ] 3215 | edge 3216 | [ 3217 | source 104 3218 | target 67 3219 | ] 3220 | edge 3221 | [ 3222 | source 104 3223 | target 41 3224 | ] 3225 | edge 3226 | [ 3227 | source 104 3228 | target 16 3229 | ] 3230 | edge 3231 | [ 3232 | source 104 3233 | target 87 3234 | ] 3235 | edge 3236 | [ 3237 | source 104 3238 | target 23 3239 | ] 3240 | edge 3241 | [ 3242 | source 104 3243 | target 4 3244 | ] 3245 | edge 3246 | [ 3247 | source 104 3248 | target 9 3249 | ] 3250 | edge 3251 | [ 3252 | source 105 3253 | target 89 3254 | ] 3255 | edge 3256 | [ 3257 | source 105 3258 | target 103 3259 | ] 3260 | edge 3261 | [ 3262 | source 105 3263 | target 33 3264 | ] 3265 | edge 3266 | [ 3267 | source 105 3268 | target 62 3269 | ] 3270 | edge 3271 | [ 3272 | source 105 3273 | target 37 3274 | ] 3275 | edge 3276 | [ 3277 | source 105 3278 | target 45 3279 | ] 3280 | edge 3281 | [ 3282 | source 105 3283 | target 1 3284 | ] 3285 | edge 3286 | [ 3287 | source 105 3288 | target 80 3289 | ] 3290 | edge 3291 | [ 3292 | source 105 3293 | target 25 3294 | ] 3295 | edge 3296 | [ 3297 | source 106 3298 | target 25 3299 | ] 3300 | edge 3301 | [ 3302 | source 106 3303 | target 56 3304 | ] 3305 | edge 3306 | [ 3307 | source 106 3308 | target 92 3309 | ] 3310 | edge 3311 | [ 3312 | source 106 3313 | target 2 3314 | ] 3315 | edge 3316 | [ 3317 | source 106 3318 | target 13 3319 | ] 3320 | edge 3321 | [ 3322 | source 106 3323 | target 32 3324 | ] 3325 | edge 3326 | [ 3327 | source 106 3328 | target 60 3329 | ] 3330 | edge 3331 | [ 3332 | source 106 3333 | target 6 3334 | ] 3335 | edge 3336 | [ 3337 | source 106 3338 | target 64 3339 | ] 3340 | edge 3341 | [ 3342 | source 106 3343 | target 15 3344 | ] 3345 | edge 3346 | [ 3347 | source 106 3348 | target 39 3349 | ] 3350 | edge 3351 | [ 3352 | source 107 3353 | target 88 3354 | ] 3355 | edge 3356 | [ 3357 | source 107 3358 | target 75 3359 | ] 3360 | edge 3361 | [ 3362 | source 107 3363 | target 98 3364 | ] 3365 | edge 3366 | [ 3367 | source 107 3368 | target 102 3369 | ] 3370 | edge 3371 | [ 3372 | source 107 3373 | target 72 3374 | ] 3375 | edge 3376 | [ 3377 | source 107 3378 | target 40 3379 | ] 3380 | edge 3381 | [ 3382 | source 107 3383 | target 81 3384 | ] 3385 | edge 3386 | [ 3387 | source 107 3388 | target 5 3389 | ] 3390 | edge 3391 | [ 3392 | source 107 3393 | target 10 3394 | ] 3395 | edge 3396 | [ 3397 | source 107 3398 | target 84 3399 | ] 3400 | edge 3401 | [ 3402 | source 108 3403 | target 4 3404 | ] 3405 | edge 3406 | [ 3407 | source 108 3408 | target 9 3409 | ] 3410 | edge 3411 | [ 3412 | source 108 3413 | target 7 3414 | ] 3415 | edge 3416 | [ 3417 | source 108 3418 | target 51 3419 | ] 3420 | edge 3421 | [ 3422 | source 108 3423 | target 77 3424 | ] 3425 | edge 3426 | [ 3427 | source 108 3428 | target 21 3429 | ] 3430 | edge 3431 | [ 3432 | source 108 3433 | target 78 3434 | ] 3435 | edge 3436 | [ 3437 | source 108 3438 | target 22 3439 | ] 3440 | edge 3441 | [ 3442 | source 108 3443 | target 68 3444 | ] 3445 | edge 3446 | [ 3447 | source 109 3448 | target 79 3449 | ] 3450 | edge 3451 | [ 3452 | source 109 3453 | target 30 3454 | ] 3455 | edge 3456 | [ 3457 | source 109 3458 | target 63 3459 | ] 3460 | edge 3461 | [ 3462 | source 109 3463 | target 1 3464 | ] 3465 | edge 3466 | [ 3467 | source 109 3468 | target 33 3469 | ] 3470 | edge 3471 | [ 3472 | source 109 3473 | target 103 3474 | ] 3475 | edge 3476 | [ 3477 | source 109 3478 | target 105 3479 | ] 3480 | edge 3481 | [ 3482 | source 109 3483 | target 45 3484 | ] 3485 | edge 3486 | [ 3487 | source 109 3488 | target 25 3489 | ] 3490 | edge 3491 | [ 3492 | source 109 3493 | target 89 3494 | ] 3495 | edge 3496 | [ 3497 | source 109 3498 | target 37 3499 | ] 3500 | edge 3501 | [ 3502 | source 110 3503 | target 67 3504 | ] 3505 | edge 3506 | [ 3507 | source 110 3508 | target 13 3509 | ] 3510 | edge 3511 | [ 3512 | source 110 3513 | target 24 3514 | ] 3515 | edge 3516 | [ 3517 | source 110 3518 | target 80 3519 | ] 3520 | edge 3521 | [ 3522 | source 110 3523 | target 88 3524 | ] 3525 | edge 3526 | [ 3527 | source 110 3528 | target 49 3529 | ] 3530 | edge 3531 | [ 3532 | source 110 3533 | target 73 3534 | ] 3535 | edge 3536 | [ 3537 | source 110 3538 | target 46 3539 | ] 3540 | edge 3541 | [ 3542 | source 110 3543 | target 83 3544 | ] 3545 | edge 3546 | [ 3547 | source 110 3548 | target 53 3549 | ] 3550 | edge 3551 | [ 3552 | source 111 3553 | target 23 3554 | ] 3555 | edge 3556 | [ 3557 | source 111 3558 | target 64 3559 | ] 3560 | edge 3561 | [ 3562 | source 111 3563 | target 46 3564 | ] 3565 | edge 3566 | [ 3567 | source 111 3568 | target 78 3569 | ] 3570 | edge 3571 | [ 3572 | source 111 3573 | target 8 3574 | ] 3575 | edge 3576 | [ 3577 | source 111 3578 | target 21 3579 | ] 3580 | edge 3581 | [ 3582 | source 111 3583 | target 51 3584 | ] 3585 | edge 3586 | [ 3587 | source 111 3588 | target 7 3589 | ] 3590 | edge 3591 | [ 3592 | source 111 3593 | target 108 3594 | ] 3595 | edge 3596 | [ 3597 | source 111 3598 | target 68 3599 | ] 3600 | edge 3601 | [ 3602 | source 111 3603 | target 77 3604 | ] 3605 | edge 3606 | [ 3607 | source 112 3608 | target 52 3609 | ] 3610 | edge 3611 | [ 3612 | source 112 3613 | target 96 3614 | ] 3615 | edge 3616 | [ 3617 | source 112 3618 | target 97 3619 | ] 3620 | edge 3621 | [ 3622 | source 112 3623 | target 57 3624 | ] 3625 | edge 3626 | [ 3627 | source 112 3628 | target 66 3629 | ] 3630 | edge 3631 | [ 3632 | source 112 3633 | target 63 3634 | ] 3635 | edge 3636 | [ 3637 | source 112 3638 | target 44 3639 | ] 3640 | edge 3641 | [ 3642 | source 112 3643 | target 92 3644 | ] 3645 | edge 3646 | [ 3647 | source 112 3648 | target 75 3649 | ] 3650 | edge 3651 | [ 3652 | source 112 3653 | target 91 3654 | ] 3655 | edge 3656 | [ 3657 | source 113 3658 | target 28 3659 | ] 3660 | edge 3661 | [ 3662 | source 113 3663 | target 20 3664 | ] 3665 | edge 3666 | [ 3667 | source 113 3668 | target 95 3669 | ] 3670 | edge 3671 | [ 3672 | source 113 3673 | target 59 3674 | ] 3675 | edge 3676 | [ 3677 | source 113 3678 | target 70 3679 | ] 3680 | edge 3681 | [ 3682 | source 113 3683 | target 17 3684 | ] 3685 | edge 3686 | [ 3687 | source 113 3688 | target 87 3689 | ] 3690 | edge 3691 | [ 3692 | source 113 3693 | target 76 3694 | ] 3695 | edge 3696 | [ 3697 | source 113 3698 | target 65 3699 | ] 3700 | edge 3701 | [ 3702 | source 113 3703 | target 96 3704 | ] 3705 | edge 3706 | [ 3707 | source 114 3708 | target 83 3709 | ] 3710 | edge 3711 | [ 3712 | source 114 3713 | target 88 3714 | ] 3715 | edge 3716 | [ 3717 | source 114 3718 | target 110 3719 | ] 3720 | edge 3721 | [ 3722 | source 114 3723 | target 53 3724 | ] 3725 | edge 3726 | [ 3727 | source 114 3728 | target 49 3729 | ] 3730 | edge 3731 | [ 3732 | source 114 3733 | target 73 3734 | ] 3735 | edge 3736 | [ 3737 | source 114 3738 | target 46 3739 | ] 3740 | edge 3741 | [ 3742 | source 114 3743 | target 67 3744 | ] 3745 | edge 3746 | [ 3747 | source 114 3748 | target 58 3749 | ] 3750 | edge 3751 | [ 3752 | source 114 3753 | target 15 3754 | ] 3755 | edge 3756 | [ 3757 | source 114 3758 | target 104 3759 | ] 3760 | ] 3761 | -------------------------------------------------------------------------------- /datasets/polbooks.gml: -------------------------------------------------------------------------------- 1 | Creator "Mark Newman on Wed Oct 18 16:42:04 2006" 2 | graph 3 | [ 4 | directed 0 5 | node 6 | [ 7 | id 0 8 | label "1000 Years for Revenge" 9 | value "n" 10 | ] 11 | node 12 | [ 13 | id 1 14 | label "Bush vs. the Beltway" 15 | value "c" 16 | ] 17 | node 18 | [ 19 | id 2 20 | label "Charlie Wilson's War" 21 | value "c" 22 | ] 23 | node 24 | [ 25 | id 3 26 | label "Losing Bin Laden" 27 | value "c" 28 | ] 29 | node 30 | [ 31 | id 4 32 | label "Sleeping With the Devil" 33 | value "n" 34 | ] 35 | node 36 | [ 37 | id 5 38 | label "The Man Who Warned America" 39 | value "c" 40 | ] 41 | node 42 | [ 43 | id 6 44 | label "Why America Slept" 45 | value "n" 46 | ] 47 | node 48 | [ 49 | id 7 50 | label "Ghost Wars" 51 | value "n" 52 | ] 53 | node 54 | [ 55 | id 8 56 | label "A National Party No More" 57 | value "c" 58 | ] 59 | node 60 | [ 61 | id 9 62 | label "Bush Country" 63 | value "c" 64 | ] 65 | node 66 | [ 67 | id 10 68 | label "Dereliction of Duty" 69 | value "c" 70 | ] 71 | node 72 | [ 73 | id 11 74 | label "Legacy" 75 | value "c" 76 | ] 77 | node 78 | [ 79 | id 12 80 | label "Off with Their Heads" 81 | value "c" 82 | ] 83 | node 84 | [ 85 | id 13 86 | label "Persecution" 87 | value "c" 88 | ] 89 | node 90 | [ 91 | id 14 92 | label "Rumsfeld's War" 93 | value "c" 94 | ] 95 | node 96 | [ 97 | id 15 98 | label "Breakdown" 99 | value "c" 100 | ] 101 | node 102 | [ 103 | id 16 104 | label "Betrayal" 105 | value "c" 106 | ] 107 | node 108 | [ 109 | id 17 110 | label "Shut Up and Sing" 111 | value "c" 112 | ] 113 | node 114 | [ 115 | id 18 116 | label "Meant To Be" 117 | value "n" 118 | ] 119 | node 120 | [ 121 | id 19 122 | label "The Right Man" 123 | value "c" 124 | ] 125 | node 126 | [ 127 | id 20 128 | label "Ten Minutes from Normal" 129 | value "c" 130 | ] 131 | node 132 | [ 133 | id 21 134 | label "Hillary's Scheme" 135 | value "c" 136 | ] 137 | node 138 | [ 139 | id 22 140 | label "The French Betrayal of America" 141 | value "c" 142 | ] 143 | node 144 | [ 145 | id 23 146 | label "Tales from the Left Coast" 147 | value "c" 148 | ] 149 | node 150 | [ 151 | id 24 152 | label "Hating America" 153 | value "c" 154 | ] 155 | node 156 | [ 157 | id 25 158 | label "The Third Terrorist" 159 | value "c" 160 | ] 161 | node 162 | [ 163 | id 26 164 | label "Endgame" 165 | value "c" 166 | ] 167 | node 168 | [ 169 | id 27 170 | label "Spin Sisters" 171 | value "c" 172 | ] 173 | node 174 | [ 175 | id 28 176 | label "All the Shah's Men" 177 | value "n" 178 | ] 179 | node 180 | [ 181 | id 29 182 | label "Dangerous Dimplomacy" 183 | value "c" 184 | ] 185 | node 186 | [ 187 | id 30 188 | label "The Price of Loyalty" 189 | value "l" 190 | ] 191 | node 192 | [ 193 | id 31 194 | label "House of Bush, House of Saud" 195 | value "l" 196 | ] 197 | node 198 | [ 199 | id 32 200 | label "The Death of Right and Wrong" 201 | value "c" 202 | ] 203 | node 204 | [ 205 | id 33 206 | label "Useful Idiots" 207 | value "c" 208 | ] 209 | node 210 | [ 211 | id 34 212 | label "The O'Reilly Factor" 213 | value "c" 214 | ] 215 | node 216 | [ 217 | id 35 218 | label "Let Freedom Ring" 219 | value "c" 220 | ] 221 | node 222 | [ 223 | id 36 224 | label "Those Who Trespass" 225 | value "c" 226 | ] 227 | node 228 | [ 229 | id 37 230 | label "Bias" 231 | value "c" 232 | ] 233 | node 234 | [ 235 | id 38 236 | label "Slander" 237 | value "c" 238 | ] 239 | node 240 | [ 241 | id 39 242 | label "The Savage Nation" 243 | value "c" 244 | ] 245 | node 246 | [ 247 | id 40 248 | label "Deliver Us from Evil" 249 | value "c" 250 | ] 251 | node 252 | [ 253 | id 41 254 | label "Give Me a Break" 255 | value "c" 256 | ] 257 | node 258 | [ 259 | id 42 260 | label "The Enemy Within" 261 | value "c" 262 | ] 263 | node 264 | [ 265 | id 43 266 | label "The Real America" 267 | value "c" 268 | ] 269 | node 270 | [ 271 | id 44 272 | label "Who's Looking Out for You?" 273 | value "c" 274 | ] 275 | node 276 | [ 277 | id 45 278 | label "The Official Handbook Vast Right Wing Conspiracy" 279 | value "c" 280 | ] 281 | node 282 | [ 283 | id 46 284 | label "Power Plays" 285 | value "n" 286 | ] 287 | node 288 | [ 289 | id 47 290 | label "Arrogance" 291 | value "c" 292 | ] 293 | node 294 | [ 295 | id 48 296 | label "The Perfect Wife" 297 | value "n" 298 | ] 299 | node 300 | [ 301 | id 49 302 | label "The Bushes" 303 | value "c" 304 | ] 305 | node 306 | [ 307 | id 50 308 | label "Things Worth Fighting For" 309 | value "c" 310 | ] 311 | node 312 | [ 313 | id 51 314 | label "Surprise, Security, the American Experience" 315 | value "n" 316 | ] 317 | node 318 | [ 319 | id 52 320 | label "Allies" 321 | value "c" 322 | ] 323 | node 324 | [ 325 | id 53 326 | label "Why Courage Matters" 327 | value "c" 328 | ] 329 | node 330 | [ 331 | id 54 332 | label "Hollywood Interrupted" 333 | value "c" 334 | ] 335 | node 336 | [ 337 | id 55 338 | label "Fighting Back" 339 | value "c" 340 | ] 341 | node 342 | [ 343 | id 56 344 | label "We Will Prevail" 345 | value "c" 346 | ] 347 | node 348 | [ 349 | id 57 350 | label "The Faith of George W Bush" 351 | value "c" 352 | ] 353 | node 354 | [ 355 | id 58 356 | label "Rise of the Vulcans" 357 | value "c" 358 | ] 359 | node 360 | [ 361 | id 59 362 | label "Downsize This!" 363 | value "l" 364 | ] 365 | node 366 | [ 367 | id 60 368 | label "Stupid White Men" 369 | value "l" 370 | ] 371 | node 372 | [ 373 | id 61 374 | label "Rush Limbaugh Is a Big Fat Idiot" 375 | value "l" 376 | ] 377 | node 378 | [ 379 | id 62 380 | label "The Best Democracy Money Can Buy" 381 | value "l" 382 | ] 383 | node 384 | [ 385 | id 63 386 | label "The Culture of Fear" 387 | value "l" 388 | ] 389 | node 390 | [ 391 | id 64 392 | label "America Unbound" 393 | value "l" 394 | ] 395 | node 396 | [ 397 | id 65 398 | label "The Choice" 399 | value "l" 400 | ] 401 | node 402 | [ 403 | id 66 404 | label "The Great Unraveling" 405 | value "l" 406 | ] 407 | node 408 | [ 409 | id 67 410 | label "Rogue Nation" 411 | value "l" 412 | ] 413 | node 414 | [ 415 | id 68 416 | label "Soft Power" 417 | value "l" 418 | ] 419 | node 420 | [ 421 | id 69 422 | label "Colossus" 423 | value "n" 424 | ] 425 | node 426 | [ 427 | id 70 428 | label "The Sorrows of Empire" 429 | value "l" 430 | ] 431 | node 432 | [ 433 | id 71 434 | label "Against All Enemies" 435 | value "l" 436 | ] 437 | node 438 | [ 439 | id 72 440 | label "American Dynasty" 441 | value "l" 442 | ] 443 | node 444 | [ 445 | id 73 446 | label "Big Lies" 447 | value "l" 448 | ] 449 | node 450 | [ 451 | id 74 452 | label "The Lies of George W. Bush" 453 | value "l" 454 | ] 455 | node 456 | [ 457 | id 75 458 | label "Worse Than Watergate" 459 | value "l" 460 | ] 461 | node 462 | [ 463 | id 76 464 | label "Plan of Attack" 465 | value "n" 466 | ] 467 | node 468 | [ 469 | id 77 470 | label "Bush at War" 471 | value "c" 472 | ] 473 | node 474 | [ 475 | id 78 476 | label "The New Pearl Harbor" 477 | value "l" 478 | ] 479 | node 480 | [ 481 | id 79 482 | label "Bushwomen" 483 | value "l" 484 | ] 485 | node 486 | [ 487 | id 80 488 | label "The Bubble of American Supremacy" 489 | value "l" 490 | ] 491 | node 492 | [ 493 | id 81 494 | label "Living History" 495 | value "l" 496 | ] 497 | node 498 | [ 499 | id 82 500 | label "The Politics of Truth" 501 | value "l" 502 | ] 503 | node 504 | [ 505 | id 83 506 | label "Fanatics and Fools" 507 | value "l" 508 | ] 509 | node 510 | [ 511 | id 84 512 | label "Bushwhacked" 513 | value "l" 514 | ] 515 | node 516 | [ 517 | id 85 518 | label "Disarming Iraq" 519 | value "l" 520 | ] 521 | node 522 | [ 523 | id 86 524 | label "Lies and the Lying Liars Who Tell Them" 525 | value "l" 526 | ] 527 | node 528 | [ 529 | id 87 530 | label "MoveOn's 50 Ways to Love Your Country" 531 | value "l" 532 | ] 533 | node 534 | [ 535 | id 88 536 | label "The Buying of the President 2004" 537 | value "l" 538 | ] 539 | node 540 | [ 541 | id 89 542 | label "Perfectly Legal" 543 | value "l" 544 | ] 545 | node 546 | [ 547 | id 90 548 | label "Hegemony or Survival" 549 | value "l" 550 | ] 551 | node 552 | [ 553 | id 91 554 | label "The Exception to the Rulers" 555 | value "l" 556 | ] 557 | node 558 | [ 559 | id 92 560 | label "Freethinkers" 561 | value "l" 562 | ] 563 | node 564 | [ 565 | id 93 566 | label "Had Enough?" 567 | value "l" 568 | ] 569 | node 570 | [ 571 | id 94 572 | label "It's Still the Economy, Stupid!" 573 | value "l" 574 | ] 575 | node 576 | [ 577 | id 95 578 | label "We're Right They're Wrong" 579 | value "l" 580 | ] 581 | node 582 | [ 583 | id 96 584 | label "What Liberal Media?" 585 | value "l" 586 | ] 587 | node 588 | [ 589 | id 97 590 | label "The Clinton Wars" 591 | value "l" 592 | ] 593 | node 594 | [ 595 | id 98 596 | label "Weapons of Mass Deception" 597 | value "l" 598 | ] 599 | node 600 | [ 601 | id 99 602 | label "Dude, Where's My Country?" 603 | value "l" 604 | ] 605 | node 606 | [ 607 | id 100 608 | label "Thieves in High Places" 609 | value "l" 610 | ] 611 | node 612 | [ 613 | id 101 614 | label "Shrub" 615 | value "l" 616 | ] 617 | node 618 | [ 619 | id 102 620 | label "Buck Up Suck Up" 621 | value "l" 622 | ] 623 | node 624 | [ 625 | id 103 626 | label "The Future of Freedom" 627 | value "n" 628 | ] 629 | node 630 | [ 631 | id 104 632 | label "Empire" 633 | value "n" 634 | ] 635 | edge 636 | [ 637 | source 1 638 | target 0 639 | ] 640 | edge 641 | [ 642 | source 2 643 | target 0 644 | ] 645 | edge 646 | [ 647 | source 3 648 | target 0 649 | ] 650 | edge 651 | [ 652 | source 3 653 | target 1 654 | ] 655 | edge 656 | [ 657 | source 4 658 | target 0 659 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833 | target 8 834 | ] 835 | edge 836 | [ 837 | source 14 838 | target 9 839 | ] 840 | edge 841 | [ 842 | source 14 843 | target 11 844 | ] 845 | edge 846 | [ 847 | source 14 848 | target 12 849 | ] 850 | edge 851 | [ 852 | source 14 853 | target 7 854 | ] 855 | edge 856 | [ 857 | source 15 858 | target 3 859 | ] 860 | edge 861 | [ 862 | source 15 863 | target 10 864 | ] 865 | edge 866 | [ 867 | source 15 868 | target 12 869 | ] 870 | edge 871 | [ 872 | source 16 873 | target 3 874 | ] 875 | edge 876 | [ 877 | source 16 878 | target 10 879 | ] 880 | edge 881 | [ 882 | source 16 883 | target 15 884 | ] 885 | edge 886 | [ 887 | source 17 888 | target 3 889 | ] 890 | edge 891 | [ 892 | source 17 893 | target 11 894 | ] 895 | edge 896 | [ 897 | source 17 898 | target 12 899 | ] 900 | edge 901 | [ 902 | source 17 903 | target 13 904 | ] 905 | edge 906 | [ 907 | source 18 908 | target 3 909 | ] 910 | edge 911 | [ 912 | source 18 913 | target 6 914 | ] 915 | edge 916 | [ 917 | source 18 918 | 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source 27 1083 | target 3 1084 | ] 1085 | edge 1086 | [ 1087 | source 27 1088 | target 8 1089 | ] 1090 | edge 1091 | [ 1092 | source 27 1093 | target 9 1094 | ] 1095 | edge 1096 | [ 1097 | source 27 1098 | target 11 1099 | ] 1100 | edge 1101 | [ 1102 | source 27 1103 | target 23 1104 | ] 1105 | edge 1106 | [ 1107 | source 28 1108 | target 4 1109 | ] 1110 | edge 1111 | [ 1112 | source 29 1113 | target 4 1114 | ] 1115 | edge 1116 | [ 1117 | source 29 1118 | target 6 1119 | ] 1120 | edge 1121 | [ 1122 | source 29 1123 | target 11 1124 | ] 1125 | edge 1126 | [ 1127 | source 29 1128 | target 13 1129 | ] 1130 | edge 1131 | [ 1132 | source 30 1133 | target 4 1134 | ] 1135 | edge 1136 | [ 1137 | source 30 1138 | target 7 1139 | ] 1140 | edge 1141 | [ 1142 | source 31 1143 | target 4 1144 | ] 1145 | edge 1146 | [ 1147 | source 31 1148 | target 30 1149 | ] 1150 | edge 1151 | [ 1152 | source 32 1153 | target 8 1154 | ] 1155 | edge 1156 | [ 1157 | source 32 1158 | target 12 1159 | ] 1160 | edge 1161 | [ 1162 | source 32 1163 | target 13 1164 | ] 1165 | edge 1166 | [ 1167 | source 32 1168 | target 23 1169 | ] 1170 | edge 1171 | [ 1172 | source 33 1173 | target 32 1174 | ] 1175 | edge 1176 | [ 1177 | source 33 1178 | target 8 1179 | ] 1180 | edge 1181 | [ 1182 | source 33 1183 | target 10 1184 | ] 1185 | edge 1186 | [ 1187 | source 33 1188 | target 12 1189 | ] 1190 | edge 1191 | [ 1192 | source 33 1193 | target 23 1194 | ] 1195 | edge 1196 | [ 1197 | source 35 1198 | target 34 1199 | ] 1200 | edge 1201 | [ 1202 | source 35 1203 | target 8 1204 | ] 1205 | edge 1206 | [ 1207 | source 35 1208 | target 10 1209 | ] 1210 | edge 1211 | [ 1212 | source 36 1213 | target 34 1214 | ] 1215 | edge 1216 | [ 1217 | source 36 1218 | target 35 1219 | ] 1220 | edge 1221 | [ 1222 | source 36 1223 | target 12 1224 | ] 1225 | edge 1226 | [ 1227 | source 37 1228 | target 34 1229 | ] 1230 | edge 1231 | [ 1232 | source 37 1233 | target 8 1234 | ] 1235 | edge 1236 | [ 1237 | source 37 1238 | target 10 1239 | ] 1240 | edge 1241 | [ 1242 | source 37 1243 | target 35 1244 | ] 1245 | edge 1246 | [ 1247 | source 37 1248 | target 33 1249 | ] 1250 | edge 1251 | [ 1252 | source 38 1253 | target 34 1254 | ] 1255 | edge 1256 | [ 1257 | source 38 1258 | target 10 1259 | ] 1260 | edge 1261 | [ 1262 | source 38 1263 | target 35 1264 | ] 1265 | edge 1266 | [ 1267 | source 38 1268 | target 12 1269 | ] 1270 | edge 1271 | [ 1272 | source 38 1273 | target 37 1274 | ] 1275 | edge 1276 | [ 1277 | source 38 1278 | target 33 1279 | ] 1280 | edge 1281 | [ 1282 | source 39 1283 | target 34 1284 | ] 1285 | edge 1286 | [ 1287 | source 39 1288 | target 10 1289 | ] 1290 | edge 1291 | [ 1292 | source 39 1293 | target 35 1294 | ] 1295 | edge 1296 | [ 1297 | source 39 1298 | target 12 1299 | ] 1300 | edge 1301 | [ 1302 | source 39 1303 | target 38 1304 | ] 1305 | edge 1306 | [ 1307 | source 39 1308 | target 33 1309 | ] 1310 | edge 1311 | [ 1312 | source 40 1313 | target 8 1314 | ] 1315 | edge 1316 | [ 1317 | source 40 1318 | target 35 1319 | ] 1320 | edge 1321 | [ 1322 | source 40 1323 | target 12 1324 | ] 1325 | edge 1326 | [ 1327 | source 40 1328 | target 13 1329 | ] 1330 | edge 1331 | [ 1332 | source 40 1333 | target 20 1334 | ] 1335 | edge 1336 | [ 1337 | source 40 1338 | target 22 1339 | ] 1340 | edge 1341 | [ 1342 | source 40 1343 | target 39 1344 | ] 1345 | edge 1346 | [ 1347 | source 40 1348 | target 24 1349 | ] 1350 | edge 1351 | [ 1352 | source 40 1353 | target 25 1354 | ] 1355 | edge 1356 | [ 1357 | source 40 1358 | target 26 1359 | ] 1360 | edge 1361 | [ 1362 | source 40 1363 | target 27 1364 | ] 1365 | edge 1366 | [ 1367 | source 41 1368 | target 8 1369 | ] 1370 | edge 1371 | [ 1372 | source 41 1373 | target 9 1374 | ] 1375 | edge 1376 | [ 1377 | source 41 1378 | target 40 1379 | ] 1380 | edge 1381 | [ 1382 | source 41 1383 | target 12 1384 | ] 1385 | edge 1386 | [ 1387 | source 41 1388 | target 36 1389 | ] 1390 | edge 1391 | [ 1392 | source 41 1393 | target 27 1394 | ] 1395 | edge 1396 | [ 1397 | source 42 1398 | target 8 1399 | ] 1400 | edge 1401 | [ 1402 | source 42 1403 | target 40 1404 | ] 1405 | edge 1406 | [ 1407 | source 42 1408 | target 13 1409 | ] 1410 | edge 1411 | [ 1412 | source 42 1413 | target 39 1414 | ] 1415 | edge 1416 | [ 1417 | source 43 1418 | target 8 1419 | ] 1420 | edge 1421 | [ 1422 | source 43 1423 | target 35 1424 | ] 1425 | edge 1426 | [ 1427 | source 43 1428 | target 13 1429 | ] 1430 | edge 1431 | [ 1432 | source 43 1433 | target 42 1434 | ] 1435 | edge 1436 | [ 1437 | source 44 1438 | target 8 1439 | ] 1440 | edge 1441 | [ 1442 | source 44 1443 | target 40 1444 | ] 1445 | edge 1446 | [ 1447 | source 44 1448 | target 35 1449 | ] 1450 | edge 1451 | [ 1452 | source 44 1453 | target 12 1454 | ] 1455 | edge 1456 | [ 1457 | source 44 1458 | target 13 1459 | ] 1460 | edge 1461 | [ 1462 | source 45 1463 | target 8 1464 | ] 1465 | edge 1466 | [ 1467 | source 45 1468 | target 9 1469 | ] 1470 | edge 1471 | [ 1472 | source 45 1473 | target 40 1474 | ] 1475 | edge 1476 | [ 1477 | source 45 1478 | target 11 1479 | ] 1480 | edge 1481 | [ 1482 | source 45 1483 | target 26 1484 | ] 1485 | edge 1486 | [ 1487 | source 46 1488 | target 8 1489 | ] 1490 | edge 1491 | [ 1492 | source 46 1493 | target 12 1494 | ] 1495 | edge 1496 | [ 1497 | source 47 1498 | target 9 1499 | ] 1500 | edge 1501 | [ 1502 | source 47 1503 | target 40 1504 | ] 1505 | edge 1506 | [ 1507 | source 47 1508 | target 41 1509 | ] 1510 | edge 1511 | [ 1512 | source 47 1513 | target 11 1514 | ] 1515 | edge 1516 | [ 1517 | source 47 1518 | target 12 1519 | ] 1520 | edge 1521 | [ 1522 | source 47 1523 | target 13 1524 | ] 1525 | edge 1526 | [ 1527 | source 47 1528 | target 42 1529 | ] 1530 | edge 1531 | [ 1532 | source 47 1533 | target 36 1534 | ] 1535 | edge 1536 | [ 1537 | source 47 1538 | target 37 1539 | ] 1540 | edge 1541 | [ 1542 | source 47 1543 | target 17 1544 | ] 1545 | edge 1546 | [ 1547 | source 47 1548 | target 33 1549 | ] 1550 | edge 1551 | [ 1552 | source 47 1553 | target 45 1554 | ] 1555 | edge 1556 | [ 1557 | source 47 1558 | target 46 1559 | ] 1560 | edge 1561 | [ 1562 | source 47 1563 | target 23 1564 | ] 1565 | edge 1566 | [ 1567 | source 47 1568 | target 24 1569 | ] 1570 | edge 1571 | [ 1572 | source 47 1573 | target 26 1574 | ] 1575 | edge 1576 | [ 1577 | source 47 1578 | target 27 1579 | ] 1580 | edge 1581 | [ 1582 | source 48 1583 | target 9 1584 | ] 1585 | edge 1586 | [ 1587 | source 48 1588 | target 20 1589 | ] 1590 | edge 1591 | [ 1592 | source 49 1593 | target 9 1594 | ] 1595 | edge 1596 | [ 1597 | source 49 1598 | target 20 1599 | ] 1600 | edge 1601 | [ 1602 | source 49 1603 | target 31 1604 | ] 1605 | edge 1606 | [ 1607 | source 49 1608 | target 48 1609 | ] 1610 | edge 1611 | [ 1612 | source 50 1613 | target 9 1614 | ] 1615 | edge 1616 | [ 1617 | source 50 1618 | target 11 1619 | ] 1620 | edge 1621 | [ 1622 | source 51 1623 | target 9 1624 | ] 1625 | edge 1626 | [ 1627 | source 52 1628 | target 9 1629 | ] 1630 | edge 1631 | [ 1632 | source 52 1633 | target 22 1634 | ] 1635 | edge 1636 | [ 1637 | source 52 1638 | target 51 1639 | ] 1640 | edge 1641 | [ 1642 | source 53 1643 | target 40 1644 | ] 1645 | edge 1646 | [ 1647 | source 53 1648 | target 20 1649 | ] 1650 | edge 1651 | [ 1652 | source 53 1653 | target 24 1654 | ] 1655 | edge 1656 | [ 1657 | source 53 1658 | target 26 1659 | ] 1660 | edge 1661 | [ 1662 | source 54 1663 | target 40 1664 | ] 1665 | edge 1666 | [ 1667 | source 54 1668 | target 41 1669 | ] 1670 | edge 1671 | [ 1672 | source 54 1673 | target 12 1674 | ] 1675 | edge 1676 | [ 1677 | source 54 1678 | target 47 1679 | ] 1680 | edge 1681 | [ 1682 | source 54 1683 | target 23 1684 | ] 1685 | edge 1686 | [ 1687 | source 54 1688 | target 27 1689 | ] 1690 | edge 1691 | [ 1692 | source 55 1693 | target 10 1694 | ] 1695 | edge 1696 | [ 1697 | source 55 1698 | target 12 1699 | ] 1700 | edge 1701 | [ 1702 | source 55 1703 | target 15 1704 | ] 1705 | edge 1706 | [ 1707 | source 55 1708 | 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2646 | [ 2647 | source 98 2648 | target 73 2649 | ] 2650 | edge 2651 | [ 2652 | source 98 2653 | target 74 2654 | ] 2655 | edge 2656 | [ 2657 | source 98 2658 | target 87 2659 | ] 2660 | edge 2661 | [ 2662 | source 98 2663 | target 91 2664 | ] 2665 | edge 2666 | [ 2667 | source 99 2668 | target 73 2669 | ] 2670 | edge 2671 | [ 2672 | source 99 2673 | target 74 2674 | ] 2675 | edge 2676 | [ 2677 | source 99 2678 | target 66 2679 | ] 2680 | edge 2681 | [ 2682 | source 99 2683 | target 84 2684 | ] 2685 | edge 2686 | [ 2687 | source 99 2688 | target 93 2689 | ] 2690 | edge 2691 | [ 2692 | source 99 2693 | target 60 2694 | ] 2695 | edge 2696 | [ 2697 | source 99 2698 | target 59 2699 | ] 2700 | edge 2701 | [ 2702 | source 99 2703 | target 30 2704 | ] 2705 | edge 2706 | [ 2707 | source 99 2708 | target 62 2709 | ] 2710 | edge 2711 | [ 2712 | source 99 2713 | target 63 2714 | ] 2715 | edge 2716 | [ 2717 | source 99 2718 | target 90 2719 | ] 2720 | edge 2721 | [ 2722 | source 100 2723 | target 73 2724 | ] 2725 | edge 2726 | [ 2727 | source 100 2728 | target 66 2729 | ] 2730 | edge 2731 | [ 2732 | source 100 2733 | target 84 2734 | ] 2735 | edge 2736 | [ 2737 | source 100 2738 | target 86 2739 | ] 2740 | edge 2741 | [ 2742 | source 100 2743 | target 96 2744 | ] 2745 | edge 2746 | [ 2747 | source 100 2748 | target 98 2749 | ] 2750 | edge 2751 | [ 2752 | source 100 2753 | target 99 2754 | ] 2755 | edge 2756 | [ 2757 | source 100 2758 | target 62 2759 | ] 2760 | edge 2761 | [ 2762 | source 100 2763 | target 79 2764 | ] 2765 | edge 2766 | [ 2767 | source 100 2768 | target 91 2769 | ] 2770 | edge 2771 | [ 2772 | source 100 2773 | target 83 2774 | ] 2775 | edge 2776 | [ 2777 | source 101 2778 | target 84 2779 | ] 2780 | edge 2781 | [ 2782 | source 101 2783 | target 86 2784 | ] 2785 | edge 2786 | [ 2787 | source 101 2788 | target 94 2789 | ] 2790 | edge 2791 | [ 2792 | source 101 2793 | target 100 2794 | ] 2795 | edge 2796 | [ 2797 | source 101 2798 | target 61 2799 | ] 2800 | edge 2801 | [ 2802 | source 102 2803 | target 93 2804 | ] 2805 | edge 2806 | [ 2807 | source 102 2808 | target 94 2809 | ] 2810 | edge 2811 | [ 2812 | source 102 2813 | target 95 2814 | ] 2815 | edge 2816 | [ 2817 | source 102 2818 | target 46 2819 | ] 2820 | edge 2821 | [ 2822 | source 103 2823 | target 67 2824 | ] 2825 | edge 2826 | [ 2827 | source 104 2828 | target 67 2829 | ] 2830 | edge 2831 | [ 2832 | source 104 2833 | target 69 2834 | ] 2835 | edge 2836 | [ 2837 | source 104 2838 | target 103 2839 | ] 2840 | ] 2841 | -------------------------------------------------------------------------------- /dolphin_dwt_ae.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Wed Mar 13 12:36:30 2019 5 | 6 | @author: dilber 7 | """ 8 | 9 | import numpy as np 10 | #import scipy.sparse as sp 11 | #import tensorflow as tf 12 | from keras.layers import Input, Dense, Dropout, Lambda 13 | from keras.models import Model 14 | from keras import optimizers 15 | from keras import backend as K 16 | 17 | from dwt_customLayer import DenseWeightTied 18 | #------------------------------------------------------------------------------ 19 | 20 | def ce(y_true, y_pred): 21 | """ Sigmoid cross-entropy loss """ 22 | return K.mean(K.binary_crossentropy(target=y_true, 23 | output=y_pred, 24 | from_logits=True), 25 | axis=-1) 26 | 27 | def mvn(tensor): 28 | """Per row mean-variance normalization.""" 29 | epsilon = 1e-6 30 | mean = K.mean(tensor, axis=1, keepdims=True) 31 | std = K.std(tensor, axis=1, keepdims=True) 32 | mvn = (tensor - mean) / (std + epsilon) 33 | return mvn 34 | 35 | def autoencoder(adj, weight = None): 36 | h, w = adj.shape 37 | 38 | kwargs = dict( 39 | use_bias=True, 40 | kernel_initializer = 'glorot_normal', 41 | kernel_regularizer = None, 42 | bias_initializer='zeros', 43 | bias_regularizer=None, 44 | trainable=True) 45 | 46 | data = Input(shape=(w,), dtype=np.float32, name='data') 47 | noisy_data = Dropout(rate=0.2, name='drop0')(data) 48 | 49 | #first set of encoding 50 | encoded = Dense(32, activation='relu', name='encoded1', **kwargs)(noisy_data) 51 | encoded = Lambda(mvn, name='mvn1')(encoded) 52 | encoded = Dropout(rate=0.2, name='drop1')(encoded) 53 | 54 | #second encoding 55 | encoded = Dense(16, activation='relu', name='encoded2', **kwargs)(encoded) 56 | encoded = Lambda(mvn, name='mvn2')(encoded) 57 | encoded = Dropout(rate=0.2, name = 'drop2')(encoded) 58 | 59 | encoder = Model([data], encoded) 60 | encoded1 = encoder.get_layer('encoded1') 61 | encoded2 = encoder.get_layer('encoded2') 62 | 63 | #first decoding 64 | decoded = DenseWeightTied(32, tie_to=encoded2, transpose=True, activation='relu', 65 | name='decoded3')(encoded) 66 | decoded = Lambda(mvn, name='mvn3')(decoded) 67 | decoded = Dropout(rate=0.2, name='d-drop2')(decoded) 68 | 69 | #second decoding 70 | decoded = DenseWeightTied(w, tie_to=encoded1, transpose=True, activation='linear', 71 | name='decoded')(decoded) 72 | decoded = Dropout(rate=0.2, name='d-drop1')(decoded) 73 | 74 | adam = optimizers.Adam(lr=0.001, decay=0.0) 75 | autoencoder = Model(inputs= [data], outputs= [decoded]) 76 | autoencoder.compile(optimizer=adam, loss=ce, metrics=['accuracy']) 77 | 78 | return encoder, autoencoder 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | -------------------------------------------------------------------------------- /dolphin_dwt_reconstruct.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Wed Mar 13 12:36:48 2019 5 | 6 | @author: dilber 7 | """ 8 | 9 | import numpy as np 10 | import networkx as nx 11 | from sklearn.cluster import KMeans 12 | from sklearn import metrics 13 | import matplotlib.pyplot as plt 14 | 15 | from dolphin_dwt_ae import autoencoder 16 | 17 | filename = '/home/dilber/Desktop/DL/network_dataset/dolphins/dolphins_gt.gml' 18 | G_dolphin = nx.read_gml(filename) 19 | B_dolphin = nx.modularity_matrix(G_dolphin) 20 | #------------------------------------------------------------------------------ 21 | encoder, ae = autoencoder(B_dolphin) 22 | 23 | epochs = 100 24 | train_batch_size = 62 25 | 26 | history = ae.fit(B_dolphin, B_dolphin, batch_size=train_batch_size, epochs= epochs) 27 | 28 | recons = encoder.predict(B_dolphin) 29 | 30 | #------------------------------------------------------------------------------ 31 | 32 | B_dolphin_X = np.array(recons) 33 | kmeans = KMeans(n_clusters=4, n_init=100, random_state=0) 34 | kmeans.fit(B_dolphin_X) 35 | X_ae = kmeans.labels_ 36 | 37 | #------------------------------------------------------------------------------ 38 | 39 | c_attributes = nx.get_node_attributes(G_dolphin, 'value') 40 | c_groups = [] 41 | 42 | for i, val in enumerate(c_attributes.values()): 43 | c_groups.append(val) 44 | 45 | X_gt = np.array(c_groups) 46 | #------------------------------------------------------------------------------ 47 | print(history.history.keys()) 48 | plt.plot(history.history['loss']) 49 | plt.title('Dolphin') 50 | plt.ylabel('loss') 51 | plt.xlabel('epoch') 52 | plt.show() 53 | 54 | #------------------------------------------------------------------------------ 55 | metrics.normalized_mutual_info_score(X_gt, X_ae, average_method='arithmetic') 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | -------------------------------------------------------------------------------- /dwt_customLayer.py: -------------------------------------------------------------------------------- 1 | import keras.backend as K 2 | from keras.layers import Layer 3 | from keras.legacy import interfaces 4 | from keras.engine import InputSpec 5 | from keras import activations, initializers, regularizers, constraints 6 | 7 | 8 | class DenseWeightTied(Layer): 9 | 10 | @interfaces.legacy_dense_support 11 | def __init__(self, units, 12 | tie_to=None, # input layer name for weight-tying 13 | transpose=False, # transpose weights from tie_to layer 14 | activation=None, 15 | use_bias=True, 16 | kernel_initializer='glorot_uniform', 17 | bias_initializer='zeros', 18 | kernel_regularizer=None, 19 | bias_regularizer=None, 20 | activity_regularizer=None, 21 | kernel_constraint=None, 22 | bias_constraint=None, 23 | trainable=True, 24 | **kwargs): 25 | if 'input_shape' not in kwargs and 'input_dim' in kwargs: 26 | kwargs['input_shape'] = (kwargs.pop('input_dim'),) 27 | super(DenseWeightTied, self).__init__(**kwargs) 28 | self.units = units 29 | # We add these two properties to save the tied weights 30 | self.tie_to = tie_to 31 | self.transpose = transpose 32 | self.activation = activations.get(activation) 33 | self.use_bias = use_bias 34 | self.kernel_initializer = initializers.get(kernel_initializer) 35 | self.bias_initializer = initializers.get(bias_initializer) 36 | self.kernel_regularizer = regularizers.get(kernel_regularizer) 37 | self.bias_regularizer = regularizers.get(bias_regularizer) 38 | self.activity_regularizer = regularizers.get(activity_regularizer) 39 | self.kernel_constraint = constraints.get(kernel_constraint) 40 | self.bias_constraint = constraints.get(bias_constraint) 41 | self.trainable = trainable 42 | self.input_spec = InputSpec(min_ndim=2) 43 | self.supports_masking = True 44 | 45 | def build(self, input_shape): 46 | assert len(input_shape) >= 2 47 | input_dim = input_shape[-1] 48 | 49 | if self.transpose: 50 | self.kernel = K.transpose(self.tie_to.kernel) 51 | else: 52 | self.kernel = self.tie_to.kernel 53 | 54 | if self.use_bias: 55 | self.bias = self.add_weight(shape=(self.units,), 56 | initializer=self.bias_initializer, 57 | name='bias', 58 | regularizer=self.bias_regularizer, 59 | constraint=self.bias_constraint, 60 | trainable=self.trainable) 61 | else: 62 | self.bias = None 63 | self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim}) 64 | self.built = True 65 | 66 | def call(self, inputs): 67 | output = K.dot(inputs, self.kernel) 68 | if self.use_bias: 69 | output = K.bias_add(output, self.bias) 70 | if self.activation is not None: 71 | output = self.activation(output) 72 | return output 73 | 74 | def compute_output_shape(self, input_shape): 75 | assert input_shape and len(input_shape) >= 2 76 | assert input_shape[-1] 77 | output_shape = list(input_shape) 78 | output_shape[-1] = self.units 79 | return tuple(output_shape) 80 | 81 | def get_config(self): 82 | config = { 83 | 'units': self.units, 84 | 'activation': activations.serialize(self.activation), 85 | 'use_bias': self.use_bias, 86 | 'kernel_initializer': initializers.serialize(self.kernel_initializer), 87 | 'bias_initializer': initializers.serialize(self.bias_initializer), 88 | 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 89 | 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 90 | 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 91 | 'kernel_constraint': constraints.serialize(self.kernel_constraint), 92 | 'bias_constraint': constraints.serialize(self.bias_constraint) 93 | } 94 | base_config = super(DenseWeightTied, self).get_config() 95 | return dict(list(base_config.items()) + list(config.items())) 96 | 97 | -------------------------------------------------------------------------------- /football_dwt_ae.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Tue Dec 18 14:06:04 2018 5 | 6 | @author: dillu 7 | """ 8 | 9 | import numpy as np 10 | from keras.layers import Input, Dense, Dropout, Lambda 11 | from keras.models import Model 12 | from keras import optimizers 13 | from keras import backend as K 14 | 15 | from dwt_customLayer import DenseWeightTied 16 | 17 | 18 | def ce(y_true, y_pred): 19 | """ Sigmoid cross-entropy loss """ 20 | return K.mean(K.binary_crossentropy(target=y_true, 21 | output=y_pred, 22 | from_logits=True), 23 | axis=-1) 24 | 25 | def mvn(tensor): 26 | """Per row mean-variance normalization.""" 27 | epsilon = 1e-6 28 | mean = K.mean(tensor, axis=1, keepdims=True) 29 | std = K.std(tensor, axis=1, keepdims=True) 30 | mvn = (tensor - mean) / (std + epsilon) 31 | return mvn 32 | 33 | 34 | def autoencoder(adj, weight=None): 35 | h, w = adj.shape 36 | 37 | kwargs = dict( 38 | use_bias=True, 39 | kernel_initializer='glorot_normal', 40 | kernel_regularizer=None, 41 | bias_initializer='zeros', 42 | bias_regularizer=None, 43 | trainable=True 44 | ) 45 | 46 | data = Input(shape=(w,), dtype=np.float32, name='data') 47 | noisy_data = Dropout(rate=0.2, name='drop0')(data) 48 | 49 | # First set of encoding transformation 50 | encoded = Dense(64, activation='relu', name='encoded1', **kwargs)(noisy_data) 51 | encoded = Lambda(mvn, name='mvn1')(encoded) 52 | encoded = Dropout(rate=0.2, name='drop1')(encoded) 53 | 54 | # Second set of encoding transformation 55 | encoded = Dense(32, activation='relu', name='encoded2', **kwargs)(encoded) 56 | encoded = Lambda(mvn, name='mvn2')(encoded) 57 | encoded = Dropout(rate=0.2, name='drop2')(encoded) 58 | 59 | encoder = Model([data], encoded) 60 | encoded1 = encoder.get_layer('encoded1') 61 | encoded2 = encoder.get_layer('encoded2') 62 | 63 | #First set of decoding transformation 64 | decoded = DenseWeightTied(64, tie_to=encoded2, transpose=True, activation='relu', 65 | name='decoded3')(encoded) 66 | decoded = Lambda(mvn, name='mvn3')(decoded) 67 | decoded = Dropout(rate=0.2, name='d-drop2')(decoded) 68 | 69 | #second set of decoding transformation 70 | decoded = DenseWeightTied(w, tie_to=encoded1, transpose=True, activation='linear', 71 | name='decoded')(decoded) 72 | decoded = Dropout(rate=0.2, name='d-drop1')(decoded) 73 | 74 | adam = optimizers.Adam(lr=0.001, decay=0.0) 75 | autoencoder = Model(inputs= [data], outputs=[decoded]) 76 | autoencoder.compile(optimizer=adam, loss=ce, metrics=['accuracy']) 77 | 78 | return encoder, autoencoder 79 | 80 | 81 | 82 | -------------------------------------------------------------------------------- /football_dwt_reconstruct.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Tue Dec 18 14:12:59 2018 5 | 6 | @author: dillu 7 | """ 8 | 9 | import numpy as np 10 | import networkx as nx 11 | from sklearn.cluster import KMeans 12 | from sklearn import metrics 13 | import matplotlib.pyplot as plt 14 | from football_dwt_ae import autoencoder 15 | 16 | filename = '/home/dilber/Desktop/DL/network_dataset/football/football.gml' 17 | G_football = nx.read_gml(filename) 18 | B_football = nx.modularity_matrix(G_football) 19 | 20 | #------------------------------------------------------------------------------ 21 | encoder, ae = autoencoder(B_football) 22 | 23 | epochs = 100 24 | train_batch_size = 64 25 | 26 | history = ae.fit(B_football, B_football, batch_size=train_batch_size, epochs=epochs) 27 | 28 | recons = encoder.predict(B_football) 29 | 30 | #------------------------------------------------------------------------------ 31 | B_football_X = np.array(recons) 32 | kmeans = KMeans(n_clusters=12, n_init=100, random_state=0) 33 | kmeans.fit(B_football_X) 34 | X_ae = kmeans.labels_ 35 | #---------------------------------------------------------|Ground Truth|------- 36 | c_attributes = nx.get_node_attributes(G_football,'value') 37 | c_groups = [] 38 | 39 | for i, val in enumerate(c_attributes.values()): 40 | c_groups.append(val) 41 | 42 | X_gt = np.array(c_groups) 43 | 44 | #------------------------------------------------------------------------------ 45 | plt.plot(history.history['loss']) 46 | plt.title('Football') 47 | plt.ylabel('loss') 48 | plt.xlabel('epoch') 49 | #--------------------------- 50 | metrics.normalized_mutual_info_score(X_gt, X_ae, average_method='arithmetic') 51 | 52 | 53 | -------------------------------------------------------------------------------- /images/Dolphin.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dilberdillu/community-detection-DL/a3969b3b49a1f53fc610ef58790404f7ad62fc93/images/Dolphin.jpg -------------------------------------------------------------------------------- /images/Football.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dilberdillu/community-detection-DL/a3969b3b49a1f53fc610ef58790404f7ad62fc93/images/Football.jpg -------------------------------------------------------------------------------- /images/Polblogs.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dilberdillu/community-detection-DL/a3969b3b49a1f53fc610ef58790404f7ad62fc93/images/Polblogs.jpg -------------------------------------------------------------------------------- /images/Polbooks.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dilberdillu/community-detection-DL/a3969b3b49a1f53fc610ef58790404f7ad62fc93/images/Polbooks.jpg -------------------------------------------------------------------------------- /images/thismodel.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dilberdillu/community-detection-DL/a3969b3b49a1f53fc610ef58790404f7ad62fc93/images/thismodel.jpg -------------------------------------------------------------------------------- /polblogs_dwt_ae.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Fri Dec 14 21:46:55 2018 5 | 6 | @author: dillu 7 | """ 8 | 9 | import numpy as np 10 | #import scipy.sparse as sp 11 | #import tensorflow as tf 12 | from keras.layers import Input, Dense, Dropout, Lambda 13 | from keras.models import Model 14 | from keras import optimizers 15 | from keras import backend as K 16 | 17 | from dwt_customLayer import DenseWeightTied 18 | 19 | 20 | def ce(y_true, y_pred): 21 | """ Sigmoid cross-entropy loss """ 22 | return K.mean(K.binary_crossentropy(target=y_true, 23 | output=y_pred, 24 | from_logits=True), 25 | axis=-1) 26 | 27 | def mvn(tensor): 28 | """Per row mean-variance normalization.""" 29 | epsilon = 1e-6 30 | mean = K.mean(tensor, axis=1, keepdims=True) 31 | std = K.std(tensor, axis=1, keepdims=True) 32 | mvn = (tensor - mean) / (std + epsilon) 33 | return mvn 34 | 35 | 36 | 37 | def autoencoder(adj, weight=None): 38 | h, w = adj.shape 39 | 40 | kwargs = dict( 41 | use_bias=True, 42 | kernel_initializer='glorot_normal', 43 | kernel_regularizer=None, 44 | bias_initializer='zeros', 45 | bias_regularizer=None, 46 | trainable=True 47 | ) 48 | 49 | data = Input(shape=(w,), dtype=np.float32, name='data') 50 | noisy_data = Dropout(rate=0.2, name='drop0')(data) 51 | 52 | # First set of encoding transformation 53 | encoded = Dense(256, activation='sigmoid', name='encoded1', **kwargs)(noisy_data) 54 | encoded = Lambda(mvn, name='mvn1')(encoded) 55 | encoded = Dropout(rate=0.2, name='drop1')(encoded) 56 | 57 | # Second set of encoding transformation 58 | encoded = Dense(128, activation='sigmoid', name='encoded2', **kwargs)(encoded) 59 | encoded = Lambda(mvn, name='mvn2')(encoded) 60 | encoded = Dropout(rate=0.2, name='drop2')(encoded) 61 | ''' 62 | # third set of encoding transformation 63 | encoded = Dense(64, activation='relu', name='encoded3', **kwargs)(encoded) 64 | #encoded = Dropout(rate=0.2, name='drop3')(encoded) 65 | ''' 66 | encoder = Model([data], encoded) 67 | encoded1 = encoder.get_layer('encoded1') 68 | encoded2 = encoder.get_layer('encoded2') 69 | #encoded3 = encoder.get_layer('encoded3') 70 | 71 | decoded = DenseWeightTied(256, tie_to=encoded2, transpose=True, activation='sigmoid', 72 | name='decoded2')(encoded) 73 | decoded = Lambda(mvn, name='mvn3')(decoded) 74 | decoded = Dropout(rate=0.2, name='d-drop2')(decoded) 75 | 76 | decoded = DenseWeightTied(w, tie_to=encoded1, transpose=True, activation='sigmoid', 77 | name='decoded1')(decoded) 78 | decoded = Dropout(rate=0.2, name='d-drop1')(decoded) 79 | ''' 80 | decoded = DenseWeightTied(w, tie_to=encoded1, transpose=True, activation='', 81 | name='decoded1')(decoded) 82 | ''' 83 | adam = optimizers.Adam(lr=0.001, decay=0.0) 84 | #sgd = optimizers.sgd(lr=0.001, decay=0.0) 85 | autoencoder = Model(inputs= [data], outputs=[decoded]) 86 | autoencoder.compile(optimizer=adam, loss=ce, metrics=['accuracy']) 87 | 88 | return encoder, autoencoder 89 | 90 | 91 | 92 | -------------------------------------------------------------------------------- /polblogs_dwt_reconstruct.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Sat Dec 15 04:28:53 2018 5 | 6 | @author: dillu 7 | """ 8 | 9 | #!/usr/bin/env python3 10 | # -*- coding: utf-8 -*- 11 | """ 12 | Created on Sat Dec 15 01:11:32 2018 13 | 14 | @author: dillu 15 | """ 16 | 17 | import numpy as np 18 | import networkx as nx 19 | from sklearn.cluster import KMeans 20 | from sklearn import metrics 21 | import matplotlib.pyplot as plt 22 | 23 | from polblogs_dwt_ae import autoencoder 24 | 25 | filename = '/home/dilber/Desktop/DL/network_dataset/polblogs/polblogs.gml' 26 | G_polblogs = nx.read_gml(filename) 27 | G_polblogs = G_polblogs.to_undirected() 28 | G_polblogs = nx.Graph(G_polblogs) 29 | B_polblogs = nx.modularity_matrix(G_polblogs) 30 | 31 | 32 | #------------------------------------------------------------------------------ 33 | encoder, ae = autoencoder(B_polblogs) 34 | 35 | epochs = 100 36 | train_batch_size = 1490 37 | history = ae.fit(B_polblogs, B_polblogs, batch_size=train_batch_size, epochs=epochs) 38 | recons = encoder.predict(B_polblogs) 39 | #--------------------------------------------------------------|Kmeans|-------- 40 | B_polblogs_X = np.array(recons) 41 | kmeans = KMeans(n_clusters=2, n_init=200, random_state=0) 42 | kmeans.fit(B_polblogs_X) 43 | X_ae = kmeans.labels_ 44 | #------------------------------------------------------------------------------ 45 | #---------------------------------------------------------------|Ground Truth|- 46 | c_attributes = nx.get_node_attributes(G_polblogs,'value') 47 | c_groups = [] 48 | 49 | for i, val in enumerate(c_attributes.values()): 50 | c_groups.append(val) 51 | 52 | X_gt = np.array(c_groups) 53 | #------------------------------------------------------------------------------ 54 | print(history.history.keys()) 55 | plt.plot(history.history['loss']) 56 | plt.title('Polblogs') 57 | plt.ylabel('loss') 58 | plt.xlabel('epoch') 59 | plt.show() 60 | 61 | #------------------------------------------------------------------------------ 62 | metrics.normalized_mutual_info_score(X_gt, X_ae, average_method='arithmetic') 63 | 64 | -------------------------------------------------------------------------------- /polbooks_dwt_ae.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Fri Dec 14 21:46:55 2018 5 | 6 | @author: dillu 7 | """ 8 | 9 | import numpy as np 10 | from keras.layers import Input, Dense, Dropout, Lambda 11 | from keras.models import Model 12 | from keras import optimizers 13 | from keras import backend as K 14 | 15 | from dwt_customLayer import DenseWeightTied 16 | 17 | 18 | def ce(y_true, y_pred): 19 | """ Sigmoid cross-entropy loss """ 20 | return K.mean(K.binary_crossentropy(target=y_true, 21 | output=y_pred, 22 | from_logits=True), 23 | axis=-1) 24 | 25 | def mvn(tensor): 26 | """Per row mean-variance normalization.""" 27 | epsilon = 1e-6 28 | mean = K.mean(tensor, axis=1, keepdims=True) 29 | std = K.std(tensor, axis=1, keepdims=True) 30 | mvn = (tensor - mean) / (std + epsilon) 31 | return mvn 32 | 33 | 34 | def autoencoder(adj, weight=None): 35 | h, w = adj.shape 36 | 37 | kwargs = dict( 38 | use_bias=True, 39 | kernel_initializer='glorot_normal', 40 | kernel_regularizer=None, 41 | bias_initializer='zeros', 42 | bias_regularizer=None, 43 | trainable=True 44 | ) 45 | 46 | data = Input(shape=(w,), dtype=np.float32, name='data') 47 | noisy_data = Dropout(rate=0.2, name='drop0')(data) 48 | 49 | # First set of encoding transformation 50 | encoded = Dense(64, activation='relu', name='encoded1', **kwargs)(noisy_data) 51 | encoded = Lambda(mvn, name='mvn1')(encoded) 52 | encoded = Dropout(rate=0.2, name='drop1')(encoded) 53 | 54 | # Second set of encoding transformation 55 | encoded = Dense(32, activation='relu', name='encoded2', **kwargs)(encoded) 56 | encoded = Lambda(mvn, name='mvn2')(encoded) 57 | encoded = Dropout(rate=0.2, name='drop2')(encoded) 58 | ''' 59 | # third set of encoding transformation 60 | encoded = Dense(16, activation='relu', name='encoded3', **kwargs)(encoded) 61 | #encoded = Dropout(rate=0.2, name='drop3')(encoded) 62 | ''' 63 | encoder = Model([data], encoded) 64 | encoded1 = encoder.get_layer('encoded1') 65 | encoded2 = encoder.get_layer('encoded2') 66 | #encoded3 = encoder.get_layer('encoded3') 67 | 68 | decoded = DenseWeightTied(64, tie_to=encoded2, transpose=True, activation='relu', 69 | name='decoded3')(encoded) 70 | decoded = Lambda(mvn, name='mvn3')(decoded) 71 | decoded = Dropout(rate=0.2, name='d-drop2')(decoded) 72 | 73 | decoded = DenseWeightTied(w, tie_to=encoded1, transpose=True, activation='linear', 74 | name='decoded')(decoded) 75 | decoded = Dropout(rate=0.2, name='d-drop1')(decoded) 76 | ''' 77 | decoded = DenseWeightTied(w, tie_to=encoded1, transpose=True, activation='linear', 78 | name='decoded1')(decoded) 79 | ''' 80 | adam = optimizers.Adam(lr=0.001, decay=0.0) 81 | autoencoder = Model(inputs= [data], outputs=[decoded]) 82 | autoencoder.compile(optimizer=adam, loss=ce, metrics=['accuracy']) 83 | 84 | return encoder, autoencoder 85 | 86 | 87 | 88 | -------------------------------------------------------------------------------- /polbooks_dwt_reconstruct.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Sat Dec 15 01:11:32 2018 5 | 6 | @author: dillu 7 | """ 8 | 9 | import numpy as np 10 | import networkx as nx 11 | from sklearn.cluster import KMeans 12 | from sklearn import metrics 13 | import matplotlib.pyplot as plt 14 | from polbooks_dwt_ae import autoencoder 15 | 16 | filename = '/home/dilber/Desktop/DL/network_dataset/polbooks/polbooks.gml' 17 | G_polbooks = nx.read_gml(filename) 18 | B_polbooks = nx.modularity_matrix(G_polbooks) 19 | 20 | #------------------------------------------------------------------------------ 21 | encoder, ae = autoencoder(B_polbooks) 22 | 23 | epochs = 100 24 | train_batch_size = 64 25 | 26 | history = ae.fit(B_polbooks, B_polbooks, batch_size=train_batch_size, epochs=epochs) 27 | 28 | recons = encoder.predict(B_polbooks) 29 | 30 | #--------------------------------------------------------------|Kmeans|-------- 31 | 32 | B_polbooks_X = np.array(recons) 33 | kmeans = KMeans(n_clusters=3, n_init=100, random_state=0) 34 | kmeans.fit(B_polbooks_X) 35 | X_ae = kmeans.labels_ 36 | #------------------------------------------------------------------------------ 37 | #---------------------------------------------------------------|GT|----------- 38 | c_attributes = nx.get_node_attributes(G_polbooks,'value') 39 | c_groups = [] 40 | 41 | for i, val in enumerate(c_attributes.values()): 42 | if val == 'l': 43 | c_groups.append(0) 44 | elif val == 'n': 45 | c_groups.append(1) 46 | else: 47 | c_groups.append(2) 48 | 49 | X_gt = np.array(c_groups) 50 | #------------------------------------------------------------------------------ 51 | plt.plot(history.history['loss']) 52 | plt.title('model loss') 53 | plt.ylabel('loss') 54 | plt.xlabel('epoch') 55 | #---------------------------------------- 56 | metrics.normalized_mutual_info_score(X_gt, X_ae, average_method='arithmetic') 57 | 58 | -------------------------------------------------------------------------------- /thesis.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dilberdillu/community-detection-DL/a3969b3b49a1f53fc610ef58790404f7ad62fc93/thesis.pdf --------------------------------------------------------------------------------