├── .gitignore ├── LICENCE ├── README.md ├── gcn ├── __init__.py ├── data │ ├── ind.citeseer.allx │ ├── ind.citeseer.ally │ ├── ind.citeseer.graph │ ├── ind.citeseer.test.index │ ├── ind.citeseer.tx │ ├── ind.citeseer.ty │ ├── ind.citeseer.x │ ├── ind.citeseer.y │ ├── ind.cora.allx │ ├── ind.cora.ally │ ├── ind.cora.graph │ ├── ind.cora.test.index │ ├── ind.cora.tx │ ├── ind.cora.ty │ ├── ind.cora.x │ ├── ind.cora.y │ ├── ind.pubmed.allx │ ├── ind.pubmed.ally │ ├── ind.pubmed.graph │ ├── ind.pubmed.test.index │ ├── ind.pubmed.tx │ ├── ind.pubmed.ty │ ├── ind.pubmed.x │ └── ind.pubmed.y ├── inits.py ├── layers.py ├── metrics.py ├── models.py ├── train.py └── utils.py ├── requirements.txt └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Custom 2 | *.idea 3 | *.png 4 | *.pdf 5 | tmp/ 6 | *.txt 7 | !requirements.txt 8 | 9 | # Byte-compiled / optimized / DLL files 10 | __pycache__/ 11 | *.py[cod] 12 | *$py.class 13 | 14 | # C extensions 15 | *.so 16 | 17 | # Distribution / packaging 18 | .Python 19 | env/ 20 | build/ 21 | develop-eggs/ 22 | dist/ 23 | downloads/ 24 | eggs/ 25 | .eggs/ 26 | lib/ 27 | lib64/ 28 | parts/ 29 | sdist/ 30 | var/ 31 | *.egg-info/ 32 | .installed.cfg 33 | *.egg 34 | 35 | # PyInstaller 36 | # Usually these files are written by a python script from a template 37 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 38 | *.manifest 39 | *.spec 40 | 41 | # Installer logs 42 | pip-log.txt 43 | pip-delete-this-directory.txt 44 | 45 | # Unit test / coverage reports 46 | htmlcov/ 47 | .tox/ 48 | .coverage 49 | .coverage.* 50 | .cache 51 | nosetests.xml 52 | coverage.xml 53 | *,cover 54 | .hypothesis/ 55 | 56 | # Translations 57 | *.mo 58 | *.pot 59 | 60 | # Django stuff: 61 | *.log 62 | local_settings.py 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # IPython Notebook 78 | .ipynb_checkpoints 79 | 80 | # pyenv 81 | .python-version 82 | 83 | # celery beat schedule file 84 | celerybeat-schedule 85 | 86 | # dotenv 87 | .env 88 | 89 | # virtualenv 90 | venv/ 91 | ENV/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | 96 | # Rope project settings 97 | .ropeproject 98 | 99 | *.pickle 100 | -------------------------------------------------------------------------------- /LICENCE: -------------------------------------------------------------------------------- 1 | The MIT License 2 | 3 | Copyright (c) 2016 Thomas Kipf 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 13 | all 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 21 | THE SOFTWARE. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Graph Convolutional Networks 2 | 3 | This is a TensorFlow implementation of Graph Convolutional Networks for the task of (semi-supervised) classification of nodes in a graph, as described in our paper: 4 | 5 | Thomas N. Kipf, Max Welling, [Semi-Supervised Classification with Graph Convolutional Networks](http://arxiv.org/abs/1609.02907) (ICLR 2017) 6 | 7 | For a high-level explanation, have a look at our blog post: 8 | 9 | Thomas Kipf, [Graph Convolutional Networks](http://tkipf.github.io/graph-convolutional-networks/) (2016) 10 | 11 | ## Installation 12 | 13 | ```bash 14 | python setup.py install 15 | ``` 16 | 17 | ## Requirements 18 | * tensorflow (>0.12) 19 | * networkx 20 | 21 | ## Run the demo 22 | 23 | ```bash 24 | cd gcn 25 | python train.py 26 | ``` 27 | 28 | ## Data 29 | 30 | In order to use your own data, you have to provide 31 | * an N by N adjacency matrix (N is the number of nodes), 32 | * an N by D feature matrix (D is the number of features per node), and 33 | * an N by E binary label matrix (E is the number of classes). 34 | 35 | Have a look at the `load_data()` function in `utils.py` for an example. 36 | 37 | In this example, we load citation network data (Cora, Citeseer or Pubmed). The original datasets can be found here: http://www.cs.umd.edu/~sen/lbc-proj/LBC.html. In our version (see `data` folder) we use dataset splits provided by https://github.com/kimiyoung/planetoid (Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov, [Revisiting Semi-Supervised Learning with Graph Embeddings](https://arxiv.org/abs/1603.08861), ICML 2016). 38 | 39 | You can specify a dataset as follows: 40 | 41 | ```bash 42 | python train.py --dataset citeseer 43 | ``` 44 | 45 | (or by editing `train.py`) 46 | 47 | ## Models 48 | 49 | You can choose between the following models: 50 | * `gcn`: Graph convolutional network (Thomas N. Kipf, Max Welling, [Semi-Supervised Classification with Graph Convolutional Networks](http://arxiv.org/abs/1609.02907), 2016) 51 | * `gcn_cheby`: Chebyshev polynomial version of graph convolutional network as described in (Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst, [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://arxiv.org/abs/1606.09375), NIPS 2016) 52 | * `dense`: Basic multi-layer perceptron that supports sparse inputs 53 | 54 | ## Graph classification 55 | 56 | Our framework also supports batch-wise classification of multiple graph instances (of potentially different size) with an adjacency matrix each. It is best to concatenate respective feature matrices and build a (sparse) block-diagonal matrix where each block corresponds to the adjacency matrix of one graph instance. For pooling (in case of graph-level outputs as opposed to node-level outputs) it is best to specify a simple pooling matrix that collects features from their respective graph instances, as illustrated below: 57 | 58 | ![graph_classification](https://user-images.githubusercontent.com/7347296/34198790-eb5bec96-e56b-11e7-90d5-157800e042de.png) 59 | 60 | 61 | ## Cite 62 | 63 | Please cite our paper if you use this code in your own work: 64 | 65 | ``` 66 | @inproceedings{kipf2017semi, 67 | title={Semi-Supervised Classification with Graph Convolutional Networks}, 68 | author={Kipf, Thomas N. and Welling, Max}, 69 | booktitle={International Conference on Learning Representations (ICLR)}, 70 | year={2017} 71 | } 72 | ``` 73 | -------------------------------------------------------------------------------- /gcn/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | from __future__ import division 3 | 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2002 72 | 2632 73 | 2554 74 | 2314 75 | 2537 76 | 1760 77 | 2088 78 | 2086 79 | 2218 80 | 2605 81 | 1953 82 | 2403 83 | 1920 84 | 2015 85 | 2335 86 | 2535 87 | 1837 88 | 2009 89 | 1905 90 | 2636 91 | 1942 92 | 2193 93 | 2576 94 | 2373 95 | 1873 96 | 2463 97 | 2509 98 | 1954 99 | 2656 100 | 2455 101 | 2494 102 | 2295 103 | 2114 104 | 2561 105 | 2176 106 | 2275 107 | 2635 108 | 2442 109 | 2704 110 | 2127 111 | 2085 112 | 2214 113 | 2487 114 | 1739 115 | 2543 116 | 1783 117 | 2485 118 | 2262 119 | 2472 120 | 2326 121 | 1738 122 | 2170 123 | 2100 124 | 2384 125 | 2152 126 | 2647 127 | 2693 128 | 2376 129 | 1775 130 | 1726 131 | 2476 132 | 2195 133 | 1773 134 | 1793 135 | 2194 136 | 2581 137 | 1854 138 | 2524 139 | 1945 140 | 1781 141 | 1987 142 | 2599 143 | 1744 144 | 2225 145 | 2300 146 | 1928 147 | 2042 148 | 2202 149 | 1958 150 | 1816 151 | 1916 152 | 2679 153 | 2190 154 | 1733 155 | 2034 156 | 2643 157 | 2177 158 | 1883 159 | 1917 160 | 1996 161 | 2491 162 | 2268 163 | 2231 164 | 2471 165 | 1919 166 | 1909 167 | 2012 168 | 2522 169 | 1865 170 | 2466 171 | 2469 172 | 2087 173 | 2584 174 | 2563 175 | 1924 176 | 2143 177 | 1736 178 | 1966 179 | 2533 180 | 2490 181 | 2630 182 | 1973 183 | 2568 184 | 1978 185 | 2664 186 | 2633 187 | 2312 188 | 2178 189 | 1754 190 | 2307 191 | 2480 192 | 1960 193 | 1742 194 | 1962 195 | 2160 196 | 2070 197 | 2553 198 | 2433 199 | 1768 200 | 2659 201 | 2379 202 | 2271 203 | 1776 204 | 2153 205 | 1877 206 | 2027 207 | 2028 208 | 2155 209 | 2196 210 | 2483 211 | 2026 212 | 2158 213 | 2407 214 | 1821 215 | 2131 216 | 2676 217 | 2277 218 | 2489 219 | 2424 220 | 1963 221 | 1808 222 | 1859 223 | 2597 224 | 2548 225 | 2368 226 | 1817 227 | 2405 228 | 2413 229 | 2603 230 | 2350 231 | 2118 232 | 2329 233 | 1969 234 | 2577 235 | 2475 236 | 2467 237 | 2425 238 | 1769 239 | 2092 240 | 2044 241 | 2586 242 | 2608 243 | 1983 244 | 2109 245 | 2649 246 | 1964 247 | 2144 248 | 1902 249 | 2411 250 | 2508 251 | 2360 252 | 1721 253 | 2005 254 | 2014 255 | 2308 256 | 2646 257 | 1949 258 | 1830 259 | 2212 260 | 2596 261 | 1832 262 | 1735 263 | 1866 264 | 2695 265 | 1941 266 | 2546 267 | 2498 268 | 2686 269 | 2665 270 | 1784 271 | 2613 272 | 1970 273 | 2021 274 | 2211 275 | 2516 276 | 2185 277 | 2479 278 | 2699 279 | 2150 280 | 1990 281 | 2063 282 | 2075 283 | 1979 284 | 2094 285 | 1787 286 | 2571 287 | 2690 288 | 1926 289 | 2341 290 | 2566 291 | 1957 292 | 1709 293 | 1955 294 | 2570 295 | 2387 296 | 1811 297 | 2025 298 | 2447 299 | 2696 300 | 2052 301 | 2366 302 | 1857 303 | 2273 304 | 2245 305 | 2672 306 | 2133 307 | 2421 308 | 1929 309 | 2125 310 | 2319 311 | 2641 312 | 2167 313 | 2418 314 | 1765 315 | 1761 316 | 1828 317 | 2188 318 | 1972 319 | 1997 320 | 2419 321 | 2289 322 | 2296 323 | 2587 324 | 2051 325 | 2440 326 | 2053 327 | 2191 328 | 1923 329 | 2164 330 | 1861 331 | 2339 332 | 2333 333 | 2523 334 | 2670 335 | 2121 336 | 1921 337 | 1724 338 | 2253 339 | 2374 340 | 1940 341 | 2545 342 | 2301 343 | 2244 344 | 2156 345 | 1849 346 | 2551 347 | 2011 348 | 2279 349 | 2572 350 | 1757 351 | 2400 352 | 2569 353 | 2072 354 | 2526 355 | 2173 356 | 2069 357 | 2036 358 | 1819 359 | 1734 360 | 1880 361 | 2137 362 | 2408 363 | 2226 364 | 2604 365 | 1771 366 | 2698 367 | 2187 368 | 2060 369 | 1756 370 | 2201 371 | 2066 372 | 2439 373 | 1844 374 | 1772 375 | 2383 376 | 2398 377 | 1708 378 | 1992 379 | 1959 380 | 1794 381 | 2426 382 | 2702 383 | 2444 384 | 1944 385 | 1829 386 | 2660 387 | 2497 388 | 2607 389 | 2343 390 | 1730 391 | 2624 392 | 1790 393 | 1935 394 | 1967 395 | 2401 396 | 2255 397 | 2355 398 | 2348 399 | 1931 400 | 2183 401 | 2161 402 | 2701 403 | 1948 404 | 2501 405 | 2192 406 | 2404 407 | 2209 408 | 2331 409 | 1810 410 | 2363 411 | 2334 412 | 1887 413 | 2393 414 | 2557 415 | 1719 416 | 1732 417 | 1986 418 | 2037 419 | 2056 420 | 1867 421 | 2126 422 | 1932 423 | 2117 424 | 1807 425 | 1801 426 | 1743 427 | 2041 428 | 1843 429 | 2388 430 | 2221 431 | 1833 432 | 2677 433 | 1778 434 | 2661 435 | 2306 436 | 2394 437 | 2106 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529 | 1713 530 | 2058 531 | 2558 532 | 1718 533 | 1864 534 | 1876 535 | 2338 536 | 1879 537 | 1891 538 | 2186 539 | 2451 540 | 2181 541 | 2638 542 | 2644 543 | 2103 544 | 2591 545 | 2266 546 | 2468 547 | 1869 548 | 2582 549 | 2674 550 | 2361 551 | 2462 552 | 1748 553 | 2215 554 | 2615 555 | 2236 556 | 2248 557 | 2493 558 | 2342 559 | 2449 560 | 2274 561 | 1824 562 | 1852 563 | 1870 564 | 2441 565 | 2356 566 | 1835 567 | 2694 568 | 2602 569 | 2685 570 | 1893 571 | 2544 572 | 2536 573 | 1994 574 | 1853 575 | 1838 576 | 1786 577 | 1930 578 | 2539 579 | 1892 580 | 2265 581 | 2618 582 | 2486 583 | 2583 584 | 2061 585 | 1796 586 | 1806 587 | 2084 588 | 1933 589 | 2095 590 | 2136 591 | 2078 592 | 1884 593 | 2438 594 | 2286 595 | 2138 596 | 1750 597 | 2184 598 | 1799 599 | 2278 600 | 2410 601 | 2642 602 | 2435 603 | 1956 604 | 2399 605 | 1774 606 | 2129 607 | 1898 608 | 1823 609 | 1938 610 | 2299 611 | 1862 612 | 2420 613 | 2673 614 | 1984 615 | 2204 616 | 1717 617 | 2074 618 | 2213 619 | 2436 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19695 48 | 19030 49 | 19523 50 | 19249 51 | 19079 52 | 19232 53 | 19455 54 | 18743 55 | 18800 56 | 19071 57 | 18885 58 | 19593 59 | 19394 60 | 19390 61 | 18832 62 | 19445 63 | 18838 64 | 19632 65 | 19548 66 | 19546 67 | 18825 68 | 19498 69 | 19266 70 | 19117 71 | 19595 72 | 19252 73 | 18730 74 | 18913 75 | 18809 76 | 19452 77 | 19520 78 | 19274 79 | 19555 80 | 19388 81 | 18919 82 | 19099 83 | 19637 84 | 19403 85 | 18720 86 | 19526 87 | 18905 88 | 19451 89 | 19408 90 | 18923 91 | 18794 92 | 19322 93 | 19431 94 | 18912 95 | 18841 96 | 19239 97 | 19125 98 | 19258 99 | 19565 100 | 18898 101 | 19482 102 | 19029 103 | 18778 104 | 19096 105 | 19684 106 | 19552 107 | 18765 108 | 19361 109 | 19171 110 | 19367 111 | 19623 112 | 19402 113 | 19327 114 | 19118 115 | 18888 116 | 18726 117 | 19510 118 | 18831 119 | 19490 120 | 19576 121 | 19050 122 | 18729 123 | 18896 124 | 19246 125 | 19012 126 | 18862 127 | 18873 128 | 19193 129 | 19693 130 | 19474 131 | 18953 132 | 19115 133 | 19182 134 | 19269 135 | 19116 136 | 18837 137 | 18872 138 | 19007 139 | 19212 140 | 18798 141 | 19102 142 | 18772 143 | 19660 144 | 19511 145 | 18914 146 | 18886 147 | 19672 148 | 19360 149 | 19213 150 | 18810 151 | 19420 152 | 19512 153 | 18719 154 | 19432 155 | 19350 156 | 19127 157 | 18782 158 | 19587 159 | 18924 160 | 19488 161 | 18781 162 | 19340 163 | 19190 164 | 19383 165 | 19094 166 | 18835 167 | 19487 168 | 19230 169 | 18791 170 | 18882 171 | 18937 172 | 18928 173 | 18755 174 | 18802 175 | 19516 176 | 18795 177 | 18786 178 | 19273 179 | 19349 180 | 19398 181 | 19626 182 | 19130 183 | 19351 184 | 19489 185 | 19446 186 | 18959 187 | 19025 188 | 18792 189 | 18878 190 | 19304 191 | 19629 192 | 19061 193 | 18785 194 | 19194 195 | 19179 196 | 19210 197 | 19417 198 | 19583 199 | 19415 200 | 19443 201 | 18739 202 | 19662 203 | 18904 204 | 18910 205 | 18901 206 | 18960 207 | 18722 208 | 18827 209 | 19290 210 | 18842 211 | 19389 212 | 19344 213 | 18961 214 | 19098 215 | 19147 216 | 19334 217 | 19358 218 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18776 302 | 19203 303 | 19158 304 | 18895 305 | 19165 306 | 19382 307 | 18780 308 | 18836 309 | 19373 310 | 19659 311 | 18947 312 | 19375 313 | 19299 314 | 18761 315 | 19366 316 | 18754 317 | 19248 318 | 19416 319 | 19658 320 | 19638 321 | 19034 322 | 19281 323 | 18844 324 | 18922 325 | 19491 326 | 19272 327 | 19341 328 | 19068 329 | 19332 330 | 19559 331 | 19293 332 | 18804 333 | 18933 334 | 18935 335 | 19405 336 | 18936 337 | 18945 338 | 18943 339 | 18818 340 | 18797 341 | 19570 342 | 19464 343 | 19428 344 | 19093 345 | 19433 346 | 18986 347 | 19161 348 | 19255 349 | 19157 350 | 19046 351 | 19292 352 | 19434 353 | 19298 354 | 18724 355 | 19410 356 | 19694 357 | 19214 358 | 19640 359 | 19189 360 | 18963 361 | 19218 362 | 19585 363 | 19041 364 | 19550 365 | 19123 366 | 19620 367 | 19376 368 | 19561 369 | 18944 370 | 19706 371 | 19056 372 | 19283 373 | 18741 374 | 19319 375 | 19144 376 | 19542 377 | 18821 378 | 19404 379 | 19080 380 | 19303 381 | 18793 382 | 19306 383 | 19678 384 | 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19424 551 | 18784 552 | 19419 553 | 19159 554 | 18865 555 | 19105 556 | 19315 557 | 19480 558 | 19664 559 | 19378 560 | 18803 561 | 19605 562 | 18870 563 | 19042 564 | 19426 565 | 18848 566 | 19223 567 | 19509 568 | 19532 569 | 18752 570 | 19691 571 | 18718 572 | 19209 573 | 19362 574 | 19090 575 | 19492 576 | 19567 577 | 19687 578 | 19018 579 | 18830 580 | 19530 581 | 19554 582 | 19119 583 | 19442 584 | 19558 585 | 19527 586 | 19427 587 | 19291 588 | 19543 589 | 19422 590 | 19142 591 | 18897 592 | 18950 593 | 19425 594 | 19002 595 | 19588 596 | 18978 597 | 19551 598 | 18930 599 | 18736 600 | 19101 601 | 19215 602 | 19150 603 | 19263 604 | 18949 605 | 18974 606 | 18759 607 | 19335 608 | 19200 609 | 19129 610 | 19328 611 | 19437 612 | 18988 613 | 19429 614 | 19368 615 | 19406 616 | 19049 617 | 18811 618 | 19296 619 | 19256 620 | 19385 621 | 19602 622 | 18770 623 | 19337 624 | 19580 625 | 19476 626 | 19045 627 | 19132 628 | 19089 629 | 19120 630 | 19265 631 | 19483 632 | 18767 633 | 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19356 966 | 19083 967 | 18926 968 | 18789 969 | 19370 970 | 18861 971 | 19311 972 | 19557 973 | 19531 974 | 19436 975 | 19140 976 | 19310 977 | 19501 978 | 18721 979 | 19604 980 | 19713 981 | 19262 982 | 19563 983 | 19507 984 | 19440 985 | 19572 986 | 19513 987 | 19515 988 | 19518 989 | 19421 990 | 19470 991 | 19499 992 | 19663 993 | 19508 994 | 18871 995 | 19528 996 | 19500 997 | 19307 998 | 19288 999 | 19594 1000 | 19271 1001 | -------------------------------------------------------------------------------- /gcn/data/ind.pubmed.tx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tkipf/gcn/39a4089fe72ad9f055ed6fdb9746abdcfebc4d81/gcn/data/ind.pubmed.tx -------------------------------------------------------------------------------- /gcn/data/ind.pubmed.ty: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tkipf/gcn/39a4089fe72ad9f055ed6fdb9746abdcfebc4d81/gcn/data/ind.pubmed.ty -------------------------------------------------------------------------------- /gcn/data/ind.pubmed.x: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tkipf/gcn/39a4089fe72ad9f055ed6fdb9746abdcfebc4d81/gcn/data/ind.pubmed.x -------------------------------------------------------------------------------- /gcn/data/ind.pubmed.y: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tkipf/gcn/39a4089fe72ad9f055ed6fdb9746abdcfebc4d81/gcn/data/ind.pubmed.y -------------------------------------------------------------------------------- /gcn/inits.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | 4 | 5 | def uniform(shape, scale=0.05, name=None): 6 | """Uniform init.""" 7 | initial = tf.random_uniform(shape, minval=-scale, maxval=scale, dtype=tf.float32) 8 | return tf.Variable(initial, name=name) 9 | 10 | 11 | def glorot(shape, name=None): 12 | """Glorot & Bengio (AISTATS 2010) init.""" 13 | init_range = np.sqrt(6.0/(shape[0]+shape[1])) 14 | initial = tf.random_uniform(shape, minval=-init_range, maxval=init_range, dtype=tf.float32) 15 | return tf.Variable(initial, name=name) 16 | 17 | 18 | def zeros(shape, name=None): 19 | """All zeros.""" 20 | initial = tf.zeros(shape, dtype=tf.float32) 21 | return tf.Variable(initial, name=name) 22 | 23 | 24 | def ones(shape, name=None): 25 | """All ones.""" 26 | initial = tf.ones(shape, dtype=tf.float32) 27 | return tf.Variable(initial, name=name) -------------------------------------------------------------------------------- /gcn/layers.py: -------------------------------------------------------------------------------- 1 | from gcn.inits import * 2 | import tensorflow as tf 3 | 4 | flags = tf.app.flags 5 | FLAGS = flags.FLAGS 6 | 7 | # global unique layer ID dictionary for layer name assignment 8 | _LAYER_UIDS = {} 9 | 10 | 11 | def get_layer_uid(layer_name=''): 12 | """Helper function, assigns unique layer IDs.""" 13 | if layer_name not in _LAYER_UIDS: 14 | _LAYER_UIDS[layer_name] = 1 15 | return 1 16 | else: 17 | _LAYER_UIDS[layer_name] += 1 18 | return _LAYER_UIDS[layer_name] 19 | 20 | 21 | def sparse_dropout(x, keep_prob, noise_shape): 22 | """Dropout for sparse tensors.""" 23 | random_tensor = keep_prob 24 | random_tensor += tf.random_uniform(noise_shape) 25 | dropout_mask = tf.cast(tf.floor(random_tensor), dtype=tf.bool) 26 | pre_out = tf.sparse_retain(x, dropout_mask) 27 | return pre_out * (1./keep_prob) 28 | 29 | 30 | def dot(x, y, sparse=False): 31 | """Wrapper for tf.matmul (sparse vs dense).""" 32 | if sparse: 33 | res = tf.sparse_tensor_dense_matmul(x, y) 34 | else: 35 | res = tf.matmul(x, y) 36 | return res 37 | 38 | 39 | class Layer(object): 40 | """Base layer class. Defines basic API for all layer objects. 41 | Implementation inspired by keras (http://keras.io). 42 | 43 | # Properties 44 | name: String, defines the variable scope of the layer. 45 | logging: Boolean, switches Tensorflow histogram logging on/off 46 | 47 | # Methods 48 | _call(inputs): Defines computation graph of layer 49 | (i.e. takes input, returns output) 50 | __call__(inputs): Wrapper for _call() 51 | _log_vars(): Log all variables 52 | """ 53 | 54 | def __init__(self, **kwargs): 55 | allowed_kwargs = {'name', 'logging'} 56 | for kwarg in kwargs.keys(): 57 | assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg 58 | name = kwargs.get('name') 59 | if not name: 60 | layer = self.__class__.__name__.lower() 61 | name = layer + '_' + str(get_layer_uid(layer)) 62 | self.name = name 63 | self.vars = {} 64 | logging = kwargs.get('logging', False) 65 | self.logging = logging 66 | self.sparse_inputs = False 67 | 68 | def _call(self, inputs): 69 | return inputs 70 | 71 | def __call__(self, inputs): 72 | with tf.name_scope(self.name): 73 | if self.logging and not self.sparse_inputs: 74 | tf.summary.histogram(self.name + '/inputs', inputs) 75 | outputs = self._call(inputs) 76 | if self.logging: 77 | tf.summary.histogram(self.name + '/outputs', outputs) 78 | return outputs 79 | 80 | def _log_vars(self): 81 | for var in self.vars: 82 | tf.summary.histogram(self.name + '/vars/' + var, self.vars[var]) 83 | 84 | 85 | class Dense(Layer): 86 | """Dense layer.""" 87 | def __init__(self, input_dim, output_dim, placeholders, dropout=0., sparse_inputs=False, 88 | act=tf.nn.relu, bias=False, featureless=False, **kwargs): 89 | super(Dense, self).__init__(**kwargs) 90 | 91 | if dropout: 92 | self.dropout = placeholders['dropout'] 93 | else: 94 | self.dropout = 0. 95 | 96 | self.act = act 97 | self.sparse_inputs = sparse_inputs 98 | self.featureless = featureless 99 | self.bias = bias 100 | 101 | # helper variable for sparse dropout 102 | self.num_features_nonzero = placeholders['num_features_nonzero'] 103 | 104 | with tf.variable_scope(self.name + '_vars'): 105 | self.vars['weights'] = glorot([input_dim, output_dim], 106 | name='weights') 107 | if self.bias: 108 | self.vars['bias'] = zeros([output_dim], name='bias') 109 | 110 | if self.logging: 111 | self._log_vars() 112 | 113 | def _call(self, inputs): 114 | x = inputs 115 | 116 | # dropout 117 | if self.sparse_inputs: 118 | x = sparse_dropout(x, 1-self.dropout, self.num_features_nonzero) 119 | else: 120 | x = tf.nn.dropout(x, 1-self.dropout) 121 | 122 | # transform 123 | output = dot(x, self.vars['weights'], sparse=self.sparse_inputs) 124 | 125 | # bias 126 | if self.bias: 127 | output += self.vars['bias'] 128 | 129 | return self.act(output) 130 | 131 | 132 | class GraphConvolution(Layer): 133 | """Graph convolution layer.""" 134 | def __init__(self, input_dim, output_dim, placeholders, dropout=0., 135 | sparse_inputs=False, act=tf.nn.relu, bias=False, 136 | featureless=False, **kwargs): 137 | super(GraphConvolution, self).__init__(**kwargs) 138 | 139 | if dropout: 140 | self.dropout = placeholders['dropout'] 141 | else: 142 | self.dropout = 0. 143 | 144 | self.act = act 145 | self.support = placeholders['support'] 146 | self.sparse_inputs = sparse_inputs 147 | self.featureless = featureless 148 | self.bias = bias 149 | 150 | # helper variable for sparse dropout 151 | self.num_features_nonzero = placeholders['num_features_nonzero'] 152 | 153 | with tf.variable_scope(self.name + '_vars'): 154 | for i in range(len(self.support)): 155 | self.vars['weights_' + str(i)] = glorot([input_dim, output_dim], 156 | name='weights_' + str(i)) 157 | if self.bias: 158 | self.vars['bias'] = zeros([output_dim], name='bias') 159 | 160 | if self.logging: 161 | self._log_vars() 162 | 163 | def _call(self, inputs): 164 | x = inputs 165 | 166 | # dropout 167 | if self.sparse_inputs: 168 | x = sparse_dropout(x, 1-self.dropout, self.num_features_nonzero) 169 | else: 170 | x = tf.nn.dropout(x, 1-self.dropout) 171 | 172 | # convolve 173 | supports = list() 174 | for i in range(len(self.support)): 175 | if not self.featureless: 176 | pre_sup = dot(x, self.vars['weights_' + str(i)], 177 | sparse=self.sparse_inputs) 178 | else: 179 | pre_sup = self.vars['weights_' + str(i)] 180 | support = dot(self.support[i], pre_sup, sparse=True) 181 | supports.append(support) 182 | output = tf.add_n(supports) 183 | 184 | # bias 185 | if self.bias: 186 | output += self.vars['bias'] 187 | 188 | return self.act(output) 189 | -------------------------------------------------------------------------------- /gcn/metrics.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | 3 | 4 | def masked_softmax_cross_entropy(preds, labels, mask): 5 | """Softmax cross-entropy loss with masking.""" 6 | loss = tf.nn.softmax_cross_entropy_with_logits(logits=preds, labels=labels) 7 | mask = tf.cast(mask, dtype=tf.float32) 8 | mask /= tf.reduce_mean(mask) 9 | loss *= mask 10 | return tf.reduce_mean(loss) 11 | 12 | 13 | def masked_accuracy(preds, labels, mask): 14 | """Accuracy with masking.""" 15 | correct_prediction = tf.equal(tf.argmax(preds, 1), tf.argmax(labels, 1)) 16 | accuracy_all = tf.cast(correct_prediction, tf.float32) 17 | mask = tf.cast(mask, dtype=tf.float32) 18 | mask /= tf.reduce_mean(mask) 19 | accuracy_all *= mask 20 | return tf.reduce_mean(accuracy_all) 21 | -------------------------------------------------------------------------------- /gcn/models.py: -------------------------------------------------------------------------------- 1 | from gcn.layers import * 2 | from gcn.metrics import * 3 | 4 | flags = tf.app.flags 5 | FLAGS = flags.FLAGS 6 | 7 | 8 | class Model(object): 9 | def __init__(self, **kwargs): 10 | allowed_kwargs = {'name', 'logging'} 11 | for kwarg in kwargs.keys(): 12 | assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg 13 | name = kwargs.get('name') 14 | if not name: 15 | name = self.__class__.__name__.lower() 16 | self.name = name 17 | 18 | logging = kwargs.get('logging', False) 19 | self.logging = logging 20 | 21 | self.vars = {} 22 | self.placeholders = {} 23 | 24 | self.layers = [] 25 | self.activations = [] 26 | 27 | self.inputs = None 28 | self.outputs = None 29 | 30 | self.loss = 0 31 | self.accuracy = 0 32 | self.optimizer = None 33 | self.opt_op = None 34 | 35 | def _build(self): 36 | raise NotImplementedError 37 | 38 | def build(self): 39 | """ Wrapper for _build() """ 40 | with tf.variable_scope(self.name): 41 | self._build() 42 | 43 | # Build sequential layer model 44 | self.activations.append(self.inputs) 45 | for layer in self.layers: 46 | hidden = layer(self.activations[-1]) 47 | self.activations.append(hidden) 48 | self.outputs = self.activations[-1] 49 | 50 | # Store model variables for easy access 51 | variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name) 52 | self.vars = {var.name: var for var in variables} 53 | 54 | # Build metrics 55 | self._loss() 56 | self._accuracy() 57 | 58 | self.opt_op = self.optimizer.minimize(self.loss) 59 | 60 | def predict(self): 61 | pass 62 | 63 | def _loss(self): 64 | raise NotImplementedError 65 | 66 | def _accuracy(self): 67 | raise NotImplementedError 68 | 69 | def save(self, sess=None): 70 | if not sess: 71 | raise AttributeError("TensorFlow session not provided.") 72 | saver = tf.train.Saver(self.vars) 73 | save_path = saver.save(sess, "tmp/%s.ckpt" % self.name) 74 | print("Model saved in file: %s" % save_path) 75 | 76 | def load(self, sess=None): 77 | if not sess: 78 | raise AttributeError("TensorFlow session not provided.") 79 | saver = tf.train.Saver(self.vars) 80 | save_path = "tmp/%s.ckpt" % self.name 81 | saver.restore(sess, save_path) 82 | print("Model restored from file: %s" % save_path) 83 | 84 | 85 | class MLP(Model): 86 | def __init__(self, placeholders, input_dim, **kwargs): 87 | super(MLP, self).__init__(**kwargs) 88 | 89 | self.inputs = placeholders['features'] 90 | self.input_dim = input_dim 91 | # self.input_dim = self.inputs.get_shape().as_list()[1] # To be supported in future Tensorflow versions 92 | self.output_dim = placeholders['labels'].get_shape().as_list()[1] 93 | self.placeholders = placeholders 94 | 95 | self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) 96 | 97 | self.build() 98 | 99 | def _loss(self): 100 | # Weight decay loss 101 | for var in self.layers[0].vars.values(): 102 | self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var) 103 | 104 | # Cross entropy error 105 | self.loss += masked_softmax_cross_entropy(self.outputs, self.placeholders['labels'], 106 | self.placeholders['labels_mask']) 107 | 108 | def _accuracy(self): 109 | self.accuracy = masked_accuracy(self.outputs, self.placeholders['labels'], 110 | self.placeholders['labels_mask']) 111 | 112 | def _build(self): 113 | self.layers.append(Dense(input_dim=self.input_dim, 114 | output_dim=FLAGS.hidden1, 115 | placeholders=self.placeholders, 116 | act=tf.nn.relu, 117 | dropout=True, 118 | sparse_inputs=True, 119 | logging=self.logging)) 120 | 121 | self.layers.append(Dense(input_dim=FLAGS.hidden1, 122 | output_dim=self.output_dim, 123 | placeholders=self.placeholders, 124 | act=lambda x: x, 125 | dropout=True, 126 | logging=self.logging)) 127 | 128 | def predict(self): 129 | return tf.nn.softmax(self.outputs) 130 | 131 | 132 | class GCN(Model): 133 | def __init__(self, placeholders, input_dim, **kwargs): 134 | super(GCN, self).__init__(**kwargs) 135 | 136 | self.inputs = placeholders['features'] 137 | self.input_dim = input_dim 138 | # self.input_dim = self.inputs.get_shape().as_list()[1] # To be supported in future Tensorflow versions 139 | self.output_dim = placeholders['labels'].get_shape().as_list()[1] 140 | self.placeholders = placeholders 141 | 142 | self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) 143 | 144 | self.build() 145 | 146 | def _loss(self): 147 | # Weight decay loss 148 | for var in self.layers[0].vars.values(): 149 | self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var) 150 | 151 | # Cross entropy error 152 | self.loss += masked_softmax_cross_entropy(self.outputs, self.placeholders['labels'], 153 | self.placeholders['labels_mask']) 154 | 155 | def _accuracy(self): 156 | self.accuracy = masked_accuracy(self.outputs, self.placeholders['labels'], 157 | self.placeholders['labels_mask']) 158 | 159 | def _build(self): 160 | 161 | self.layers.append(GraphConvolution(input_dim=self.input_dim, 162 | output_dim=FLAGS.hidden1, 163 | placeholders=self.placeholders, 164 | act=tf.nn.relu, 165 | dropout=True, 166 | sparse_inputs=True, 167 | logging=self.logging)) 168 | 169 | self.layers.append(GraphConvolution(input_dim=FLAGS.hidden1, 170 | output_dim=self.output_dim, 171 | placeholders=self.placeholders, 172 | act=lambda x: x, 173 | dropout=True, 174 | logging=self.logging)) 175 | 176 | def predict(self): 177 | return tf.nn.softmax(self.outputs) 178 | -------------------------------------------------------------------------------- /gcn/train.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | from __future__ import print_function 3 | 4 | import time 5 | import tensorflow as tf 6 | 7 | from gcn.utils import * 8 | from gcn.models import GCN, MLP 9 | 10 | # Set random seed 11 | seed = 123 12 | np.random.seed(seed) 13 | tf.set_random_seed(seed) 14 | 15 | # Settings 16 | flags = tf.app.flags 17 | FLAGS = flags.FLAGS 18 | flags.DEFINE_string('dataset', 'cora', 'Dataset string.') # 'cora', 'citeseer', 'pubmed' 19 | flags.DEFINE_string('model', 'gcn', 'Model string.') # 'gcn', 'gcn_cheby', 'dense' 20 | flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.') 21 | flags.DEFINE_integer('epochs', 200, 'Number of epochs to train.') 22 | flags.DEFINE_integer('hidden1', 16, 'Number of units in hidden layer 1.') 23 | flags.DEFINE_float('dropout', 0.5, 'Dropout rate (1 - keep probability).') 24 | flags.DEFINE_float('weight_decay', 5e-4, 'Weight for L2 loss on embedding matrix.') 25 | flags.DEFINE_integer('early_stopping', 10, 'Tolerance for early stopping (# of epochs).') 26 | flags.DEFINE_integer('max_degree', 3, 'Maximum Chebyshev polynomial degree.') 27 | 28 | # Load data 29 | adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = load_data(FLAGS.dataset) 30 | 31 | # Some preprocessing 32 | features = preprocess_features(features) 33 | if FLAGS.model == 'gcn': 34 | support = [preprocess_adj(adj)] 35 | num_supports = 1 36 | model_func = GCN 37 | elif FLAGS.model == 'gcn_cheby': 38 | support = chebyshev_polynomials(adj, FLAGS.max_degree) 39 | num_supports = 1 + FLAGS.max_degree 40 | model_func = GCN 41 | elif FLAGS.model == 'dense': 42 | support = [preprocess_adj(adj)] # Not used 43 | num_supports = 1 44 | model_func = MLP 45 | else: 46 | raise ValueError('Invalid argument for model: ' + str(FLAGS.model)) 47 | 48 | # Define placeholders 49 | placeholders = { 50 | 'support': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)], 51 | 'features': tf.sparse_placeholder(tf.float32, shape=tf.constant(features[2], dtype=tf.int64)), 52 | 'labels': tf.placeholder(tf.float32, shape=(None, y_train.shape[1])), 53 | 'labels_mask': tf.placeholder(tf.int32), 54 | 'dropout': tf.placeholder_with_default(0., shape=()), 55 | 'num_features_nonzero': tf.placeholder(tf.int32) # helper variable for sparse dropout 56 | } 57 | 58 | # Create model 59 | model = model_func(placeholders, input_dim=features[2][1], logging=True) 60 | 61 | # Initialize session 62 | sess = tf.Session() 63 | 64 | 65 | # Define model evaluation function 66 | def evaluate(features, support, labels, mask, placeholders): 67 | t_test = time.time() 68 | feed_dict_val = construct_feed_dict(features, support, labels, mask, placeholders) 69 | outs_val = sess.run([model.loss, model.accuracy], feed_dict=feed_dict_val) 70 | return outs_val[0], outs_val[1], (time.time() - t_test) 71 | 72 | 73 | # Init variables 74 | sess.run(tf.global_variables_initializer()) 75 | 76 | cost_val = [] 77 | 78 | # Train model 79 | for epoch in range(FLAGS.epochs): 80 | 81 | t = time.time() 82 | # Construct feed dictionary 83 | feed_dict = construct_feed_dict(features, support, y_train, train_mask, placeholders) 84 | feed_dict.update({placeholders['dropout']: FLAGS.dropout}) 85 | 86 | # Training step 87 | outs = sess.run([model.opt_op, model.loss, model.accuracy], feed_dict=feed_dict) 88 | 89 | # Validation 90 | cost, acc, duration = evaluate(features, support, y_val, val_mask, placeholders) 91 | cost_val.append(cost) 92 | 93 | # Print results 94 | print("Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(outs[1]), 95 | "train_acc=", "{:.5f}".format(outs[2]), "val_loss=", "{:.5f}".format(cost), 96 | "val_acc=", "{:.5f}".format(acc), "time=", "{:.5f}".format(time.time() - t)) 97 | 98 | if epoch > FLAGS.early_stopping and cost_val[-1] > np.mean(cost_val[-(FLAGS.early_stopping+1):-1]): 99 | print("Early stopping...") 100 | break 101 | 102 | print("Optimization Finished!") 103 | 104 | # Testing 105 | test_cost, test_acc, test_duration = evaluate(features, support, y_test, test_mask, placeholders) 106 | print("Test set results:", "cost=", "{:.5f}".format(test_cost), 107 | "accuracy=", "{:.5f}".format(test_acc), "time=", "{:.5f}".format(test_duration)) 108 | -------------------------------------------------------------------------------- /gcn/utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pickle as pkl 3 | import networkx as nx 4 | import scipy.sparse as sp 5 | from scipy.sparse.linalg.eigen.arpack import eigsh 6 | import sys 7 | 8 | 9 | def parse_index_file(filename): 10 | """Parse index file.""" 11 | index = [] 12 | for line in open(filename): 13 | index.append(int(line.strip())) 14 | return index 15 | 16 | 17 | def sample_mask(idx, l): 18 | """Create mask.""" 19 | mask = np.zeros(l) 20 | mask[idx] = 1 21 | return np.array(mask, dtype=np.bool) 22 | 23 | 24 | def load_data(dataset_str): 25 | """ 26 | Loads input data from gcn/data directory 27 | 28 | ind.dataset_str.x => the feature vectors of the training instances as scipy.sparse.csr.csr_matrix object; 29 | ind.dataset_str.tx => the feature vectors of the test instances as scipy.sparse.csr.csr_matrix object; 30 | ind.dataset_str.allx => the feature vectors of both labeled and unlabeled training instances 31 | (a superset of ind.dataset_str.x) as scipy.sparse.csr.csr_matrix object; 32 | ind.dataset_str.y => the one-hot labels of the labeled training instances as numpy.ndarray object; 33 | ind.dataset_str.ty => the one-hot labels of the test instances as numpy.ndarray object; 34 | ind.dataset_str.ally => the labels for instances in ind.dataset_str.allx as numpy.ndarray object; 35 | ind.dataset_str.graph => a dict in the format {index: [index_of_neighbor_nodes]} as collections.defaultdict 36 | object; 37 | ind.dataset_str.test.index => the indices of test instances in graph, for the inductive setting as list object. 38 | 39 | All objects above must be saved using python pickle module. 40 | 41 | :param dataset_str: Dataset name 42 | :return: All data input files loaded (as well the training/test data). 43 | """ 44 | names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph'] 45 | objects = [] 46 | for i in range(len(names)): 47 | with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f: 48 | if sys.version_info > (3, 0): 49 | objects.append(pkl.load(f, encoding='latin1')) 50 | else: 51 | objects.append(pkl.load(f)) 52 | 53 | x, y, tx, ty, allx, ally, graph = tuple(objects) 54 | test_idx_reorder = parse_index_file("data/ind.{}.test.index".format(dataset_str)) 55 | test_idx_range = np.sort(test_idx_reorder) 56 | 57 | if dataset_str == 'citeseer': 58 | # Fix citeseer dataset (there are some isolated nodes in the graph) 59 | # Find isolated nodes, add them as zero-vecs into the right position 60 | test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1) 61 | tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1])) 62 | tx_extended[test_idx_range-min(test_idx_range), :] = tx 63 | tx = tx_extended 64 | ty_extended = np.zeros((len(test_idx_range_full), y.shape[1])) 65 | ty_extended[test_idx_range-min(test_idx_range), :] = ty 66 | ty = ty_extended 67 | 68 | features = sp.vstack((allx, tx)).tolil() 69 | features[test_idx_reorder, :] = features[test_idx_range, :] 70 | adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph)) 71 | 72 | labels = np.vstack((ally, ty)) 73 | labels[test_idx_reorder, :] = labels[test_idx_range, :] 74 | 75 | idx_test = test_idx_range.tolist() 76 | idx_train = range(len(y)) 77 | idx_val = range(len(y), len(y)+500) 78 | 79 | train_mask = sample_mask(idx_train, labels.shape[0]) 80 | val_mask = sample_mask(idx_val, labels.shape[0]) 81 | test_mask = sample_mask(idx_test, labels.shape[0]) 82 | 83 | y_train = np.zeros(labels.shape) 84 | y_val = np.zeros(labels.shape) 85 | y_test = np.zeros(labels.shape) 86 | y_train[train_mask, :] = labels[train_mask, :] 87 | y_val[val_mask, :] = labels[val_mask, :] 88 | y_test[test_mask, :] = labels[test_mask, :] 89 | 90 | return adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask 91 | 92 | 93 | def sparse_to_tuple(sparse_mx): 94 | """Convert sparse matrix to tuple representation.""" 95 | def to_tuple(mx): 96 | if not sp.isspmatrix_coo(mx): 97 | mx = mx.tocoo() 98 | coords = np.vstack((mx.row, mx.col)).transpose() 99 | values = mx.data 100 | shape = mx.shape 101 | return coords, values, shape 102 | 103 | if isinstance(sparse_mx, list): 104 | for i in range(len(sparse_mx)): 105 | sparse_mx[i] = to_tuple(sparse_mx[i]) 106 | else: 107 | sparse_mx = to_tuple(sparse_mx) 108 | 109 | return sparse_mx 110 | 111 | 112 | def preprocess_features(features): 113 | """Row-normalize feature matrix and convert to tuple representation""" 114 | rowsum = np.array(features.sum(1)) 115 | r_inv = np.power(rowsum, -1).flatten() 116 | r_inv[np.isinf(r_inv)] = 0. 117 | r_mat_inv = sp.diags(r_inv) 118 | features = r_mat_inv.dot(features) 119 | return sparse_to_tuple(features) 120 | 121 | 122 | def normalize_adj(adj): 123 | """Symmetrically normalize adjacency matrix.""" 124 | adj = sp.coo_matrix(adj) 125 | rowsum = np.array(adj.sum(1)) 126 | d_inv_sqrt = np.power(rowsum, -0.5).flatten() 127 | d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. 128 | d_mat_inv_sqrt = sp.diags(d_inv_sqrt) 129 | return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo() 130 | 131 | 132 | def preprocess_adj(adj): 133 | """Preprocessing of adjacency matrix for simple GCN model and conversion to tuple representation.""" 134 | adj_normalized = normalize_adj(adj + sp.eye(adj.shape[0])) 135 | return sparse_to_tuple(adj_normalized) 136 | 137 | 138 | def construct_feed_dict(features, support, labels, labels_mask, placeholders): 139 | """Construct feed dictionary.""" 140 | feed_dict = dict() 141 | feed_dict.update({placeholders['labels']: labels}) 142 | feed_dict.update({placeholders['labels_mask']: labels_mask}) 143 | feed_dict.update({placeholders['features']: features}) 144 | feed_dict.update({placeholders['support'][i]: support[i] for i in range(len(support))}) 145 | feed_dict.update({placeholders['num_features_nonzero']: features[1].shape}) 146 | return feed_dict 147 | 148 | 149 | def chebyshev_polynomials(adj, k): 150 | """Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation).""" 151 | print("Calculating Chebyshev polynomials up to order {}...".format(k)) 152 | 153 | adj_normalized = normalize_adj(adj) 154 | laplacian = sp.eye(adj.shape[0]) - adj_normalized 155 | largest_eigval, _ = eigsh(laplacian, 1, which='LM') 156 | scaled_laplacian = (2. / largest_eigval[0]) * laplacian - sp.eye(adj.shape[0]) 157 | 158 | t_k = list() 159 | t_k.append(sp.eye(adj.shape[0])) 160 | t_k.append(scaled_laplacian) 161 | 162 | def chebyshev_recurrence(t_k_minus_one, t_k_minus_two, scaled_lap): 163 | s_lap = sp.csr_matrix(scaled_lap, copy=True) 164 | return 2 * s_lap.dot(t_k_minus_one) - t_k_minus_two 165 | 166 | for i in range(2, k+1): 167 | t_k.append(chebyshev_recurrence(t_k[-1], t_k[-2], scaled_laplacian)) 168 | 169 | return sparse_to_tuple(t_k) 170 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | networkx==2.2 2 | scipy==1.1.0 3 | setuptools==40.6.3 4 | numpy==1.15.4 5 | tensorflow==1.15.4 6 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | from setuptools import find_packages 3 | 4 | setup(name='gcn', 5 | version='1.0', 6 | description='Graph Convolutional Networks in Tensorflow', 7 | author='Thomas Kipf', 8 | author_email='thomas.kipf@gmail.com', 9 | url='https://tkipf.github.io', 10 | download_url='https://github.com/tkipf/gcn', 11 | license='MIT', 12 | install_requires=['numpy>=1.15.4', 13 | 'tensorflow>=1.15.2,<2.0', 14 | 'networkx>=2.2', 15 | 'scipy>=1.1.0' 16 | ], 17 | package_data={'gcn': ['README.md']}, 18 | packages=find_packages()) 19 | --------------------------------------------------------------------------------