├── ARGA ├── ARGA_FLOW.pdf ├── _gitignore └── arga │ ├── __init__.py │ ├── __pycache__ │ ├── __init__.cpython-36.pyc │ ├── clustering.cpython-36.pyc │ ├── initializations.cpython-36.pyc │ ├── input_data.cpython-36.pyc │ ├── layers.cpython-36.pyc │ ├── link_prediction.cpython-36.pyc │ ├── model.cpython-36.pyc │ ├── optimizer.cpython-36.pyc │ └── preprocessing.cpython-36.pyc │ ├── clustering.py │ ├── constructor.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 │ ├── initializations.py │ ├── input_data.py │ ├── layers.py │ ├── link_prediction.py │ ├── metrics.py │ ├── model.py │ ├── optimizer.py │ ├── preprocessing.py │ ├── run.py │ └── settings.py ├── ARGA_FLOW.jpg ├── LICENCE ├── README.md └── requirements.txt /ARGA/ARGA_FLOW.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/ARGA_FLOW.pdf -------------------------------------------------------------------------------- /ARGA/_gitignore: -------------------------------------------------------------------------------- 1 | .idea/ 2 | *.pyc 3 | build/ 4 | dist/ 5 | *.egg-info/ -------------------------------------------------------------------------------- /ARGA/arga/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | from __future__ import division 3 | 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flags.FLAGS 14 | 15 | class Clustering_Runner(): 16 | def __init__(self, settings): 17 | 18 | print("Clustering on dataset: %s, model: %s, number of iteration: %3d" % (settings['data_name'], settings['model'], settings['iterations'])) 19 | 20 | self.data_name = settings['data_name'] 21 | self.iteration =settings['iterations'] 22 | self.model = settings['model'] 23 | self.n_clusters = settings['clustering_num'] 24 | 25 | def erun(self): 26 | model_str = self.model 27 | 28 | # formatted data 29 | feas = format_data(self.data_name) 30 | 31 | # Define placeholders 32 | placeholders = get_placeholder(feas['adj']) 33 | 34 | # construct model 35 | d_real, discriminator, ae_model = get_model(model_str, placeholders, feas['num_features'], feas['num_nodes'], feas['features_nonzero']) 36 | 37 | # Optimizer 38 | opt = get_optimizer(model_str, ae_model, discriminator, placeholders, feas['pos_weight'], feas['norm'], d_real, feas['num_nodes']) 39 | 40 | # Initialize session 41 | sess = tf.Session() 42 | # sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) 43 | sess.run(tf.global_variables_initializer()) 44 | 45 | # Train model 46 | for epoch in range(self.iteration): 47 | emb, _ = update(ae_model, opt, sess, feas['adj_norm'], feas['adj_label'], feas['features'], placeholders, feas['adj']) 48 | 49 | if (epoch+1) % 2 == 0: 50 | kmeans = KMeans(n_clusters=self.n_clusters, random_state=0).fit(emb) 51 | print("Epoch:", '%04d' % (epoch + 1)) 52 | predict_labels = kmeans.predict(emb) 53 | cm = clustering_metrics(feas['true_labels'], predict_labels) 54 | cm.evaluationClusterModelFromLabel() -------------------------------------------------------------------------------- /ARGA/arga/constructor.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | from model import ARGA, ARVGA, Discriminator 4 | from optimizer import OptimizerAE, OptimizerVAE 5 | import scipy.sparse as sp 6 | from input_data import load_data 7 | import inspect 8 | from preprocessing import preprocess_graph, sparse_to_tuple, mask_test_edges, construct_feed_dict 9 | flags = tf.app.flags 10 | FLAGS = flags.FLAGS 11 | 12 | def get_placeholder(adj): 13 | placeholders = { 14 | 'features': tf.sparse_placeholder(tf.float32), 15 | 'adj': tf.sparse_placeholder(tf.float32), 16 | 'adj_orig': tf.sparse_placeholder(tf.float32), 17 | 'dropout': tf.placeholder_with_default(0., shape=()), 18 | 'real_distribution': tf.placeholder(dtype=tf.float32, shape=[adj.shape[0], FLAGS.hidden2], 19 | name='real_distribution') 20 | 21 | } 22 | 23 | return placeholders 24 | 25 | 26 | def get_model(model_str, placeholders, num_features, num_nodes, features_nonzero): 27 | discriminator = Discriminator() 28 | d_real = discriminator.construct(placeholders['real_distribution']) 29 | model = None 30 | if model_str == 'arga_ae': 31 | model = ARGA(placeholders, num_features, features_nonzero) 32 | 33 | elif model_str == 'arga_vae': 34 | model = ARVGA(placeholders, num_features, num_nodes, features_nonzero) 35 | 36 | return d_real, discriminator, model 37 | 38 | 39 | def format_data(data_name): 40 | # Load data 41 | 42 | adj, features, y_test, tx, ty, test_maks, true_labels = load_data(data_name) 43 | 44 | 45 | # Store original adjacency matrix (without diagonal entries) for later 46 | adj_orig = adj 47 | adj_orig = adj_orig - sp.dia_matrix((adj_orig.diagonal()[np.newaxis, :], [0]), shape=adj_orig.shape) 48 | adj_orig.eliminate_zeros() 49 | 50 | adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false = mask_test_edges(adj) 51 | adj = adj_train 52 | 53 | if FLAGS.features == 0: 54 | features = sp.identity(features.shape[0]) # featureless 55 | 56 | # Some preprocessing 57 | adj_norm = preprocess_graph(adj) 58 | 59 | num_nodes = adj.shape[0] 60 | 61 | features = sparse_to_tuple(features.tocoo()) 62 | num_features = features[2][1] 63 | features_nonzero = features[1].shape[0] 64 | 65 | pos_weight = float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum() 66 | norm = adj.shape[0] * adj.shape[0] / float((adj.shape[0] * adj.shape[0] - adj.sum()) * 2) 67 | 68 | adj_label = adj_train + sp.eye(adj_train.shape[0]) 69 | adj_label = sparse_to_tuple(adj_label) 70 | items = [adj, num_features, num_nodes, features_nonzero, pos_weight, norm, adj_norm, adj_label, features, true_labels, train_edges, val_edges, val_edges_false, test_edges, test_edges_false, adj_orig] 71 | feas = {} 72 | for item in items: 73 | # item_name = [ k for k,v in locals().iteritems() if v == item][0] 74 | feas[retrieve_name(item)] = item 75 | 76 | 77 | return feas 78 | 79 | def get_optimizer(model_str, model, discriminator, placeholders, pos_weight, norm, d_real,num_nodes): 80 | if model_str == 'arga_ae': 81 | d_fake = discriminator.construct(model.embeddings, reuse=True) 82 | opt = OptimizerAE(preds=model.reconstructions, 83 | labels=tf.reshape(tf.sparse_tensor_to_dense(placeholders['adj_orig'], 84 | validate_indices=False), [-1]), 85 | pos_weight=pos_weight, 86 | norm=norm, 87 | d_real=d_real, 88 | d_fake=d_fake) 89 | elif model_str == 'arga_vae': 90 | opt = OptimizerVAE(preds=model.reconstructions, 91 | labels=tf.reshape(tf.sparse_tensor_to_dense(placeholders['adj_orig'], 92 | validate_indices=False), [-1]), 93 | model=model, num_nodes=num_nodes, 94 | pos_weight=pos_weight, 95 | norm=norm, 96 | d_real=d_real, 97 | d_fake=discriminator.construct(model.embeddings, reuse=True)) 98 | return opt 99 | 100 | def update(model, opt, sess, adj_norm, adj_label, features, placeholders, adj): 101 | # Construct feed dictionary 102 | feed_dict = construct_feed_dict(adj_norm, adj_label, features, placeholders) 103 | feed_dict.update({placeholders['dropout']: FLAGS.dropout}) 104 | 105 | feed_dict.update({placeholders['dropout']: 0}) 106 | emb = sess.run(model.z_mean, feed_dict=feed_dict) 107 | 108 | z_real_dist = np.random.randn(adj.shape[0], FLAGS.hidden2) 109 | feed_dict.update({placeholders['real_distribution']: z_real_dist}) 110 | 111 | for j in range(5): 112 | _, reconstruct_loss = sess.run([opt.opt_op, opt.cost], feed_dict=feed_dict) 113 | d_loss, _ = sess.run([opt.dc_loss, opt.discriminator_optimizer], feed_dict=feed_dict) 114 | g_loss, _ = sess.run([opt.generator_loss, opt.generator_optimizer], feed_dict=feed_dict) 115 | 116 | avg_cost = reconstruct_loss 117 | 118 | return emb, avg_cost 119 | 120 | 121 | def retrieve_name(var): 122 | callers_local_vars = inspect.currentframe().f_back.f_locals.items() 123 | return [var_name for var_name, var_val in callers_local_vars if var_val is var][0] -------------------------------------------------------------------------------- /ARGA/arga/data/ind.citeseer.allx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.citeseer.allx 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934 | 2745 935 | 2714 936 | 3160 937 | 3124 938 | 2849 939 | 2676 940 | 2872 941 | 3287 942 | 3189 943 | 2716 944 | 3115 945 | 2928 946 | 2871 947 | 2591 948 | 2717 949 | 2546 950 | 2777 951 | 3298 952 | 2397 953 | 3187 954 | 2726 955 | 2336 956 | 3268 957 | 2477 958 | 2904 959 | 2846 960 | 3121 961 | 2899 962 | 2510 963 | 2806 964 | 2963 965 | 3313 966 | 2679 967 | 3302 968 | 2663 969 | 3053 970 | 2469 971 | 2999 972 | 3311 973 | 2470 974 | 2638 975 | 3120 976 | 3171 977 | 2689 978 | 2922 979 | 2607 980 | 2721 981 | 2993 982 | 2887 983 | 2837 984 | 2929 985 | 2829 986 | 3234 987 | 2649 988 | 2337 989 | 2759 990 | 2778 991 | 2771 992 | 2404 993 | 2589 994 | 3123 995 | 3209 996 | 2729 997 | 3252 998 | 2606 999 | 2579 1000 | 2552 1001 | -------------------------------------------------------------------------------- /ARGA/arga/data/ind.citeseer.tx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.citeseer.tx -------------------------------------------------------------------------------- /ARGA/arga/data/ind.citeseer.ty: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.citeseer.ty -------------------------------------------------------------------------------- /ARGA/arga/data/ind.citeseer.x: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.citeseer.x -------------------------------------------------------------------------------- /ARGA/arga/data/ind.citeseer.y: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.citeseer.y -------------------------------------------------------------------------------- /ARGA/arga/data/ind.cora.allx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.cora.allx -------------------------------------------------------------------------------- /ARGA/arga/data/ind.cora.ally: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.cora.ally -------------------------------------------------------------------------------- /ARGA/arga/data/ind.cora.graph: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.cora.graph -------------------------------------------------------------------------------- /ARGA/arga/data/ind.cora.test.index: -------------------------------------------------------------------------------- 1 | 2692 2 | 2532 3 | 2050 4 | 1715 5 | 2362 6 | 2609 7 | 2622 8 | 1975 9 | 2081 10 | 1767 11 | 2263 12 | 1725 13 | 2588 14 | 2259 15 | 2357 16 | 1998 17 | 2574 18 | 2179 19 | 2291 20 | 2382 21 | 1812 22 | 1751 23 | 2422 24 | 1937 25 | 2631 26 | 2510 27 | 2378 28 | 2589 29 | 2345 30 | 1943 31 | 1850 32 | 2298 33 | 1825 34 | 2035 35 | 2507 36 | 2313 37 | 1906 38 | 1797 39 | 2023 40 | 2159 41 | 2495 42 | 1886 43 | 2122 44 | 2369 45 | 2461 46 | 1925 47 | 2565 48 | 1858 49 | 2234 50 | 2000 51 | 1846 52 | 2318 53 | 1723 54 | 2559 55 | 2258 56 | 1763 57 | 1991 58 | 1922 59 | 2003 60 | 2662 61 | 2250 62 | 2064 63 | 2529 64 | 1888 65 | 2499 66 | 2454 67 | 2320 68 | 2287 69 | 2203 70 | 2018 71 | 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 438 | 2430 439 | 2371 440 | 2606 441 | 2353 442 | 2269 443 | 2317 444 | 2645 445 | 2372 446 | 2550 447 | 2043 448 | 1968 449 | 2165 450 | 2310 451 | 1985 452 | 2446 453 | 1982 454 | 2377 455 | 2207 456 | 1818 457 | 1913 458 | 1766 459 | 1722 460 | 1894 461 | 2020 462 | 1881 463 | 2621 464 | 2409 465 | 2261 466 | 2458 467 | 2096 468 | 1712 469 | 2594 470 | 2293 471 | 2048 472 | 2359 473 | 1839 474 | 2392 475 | 2254 476 | 1911 477 | 2101 478 | 2367 479 | 1889 480 | 1753 481 | 2555 482 | 2246 483 | 2264 484 | 2010 485 | 2336 486 | 2651 487 | 2017 488 | 2140 489 | 1842 490 | 2019 491 | 1890 492 | 2525 493 | 2134 494 | 2492 495 | 2652 496 | 2040 497 | 2145 498 | 2575 499 | 2166 500 | 1999 501 | 2434 502 | 1711 503 | 2276 504 | 2450 505 | 2389 506 | 2669 507 | 2595 508 | 1814 509 | 2039 510 | 2502 511 | 1896 512 | 2168 513 | 2344 514 | 2637 515 | 2031 516 | 1977 517 | 2380 518 | 1936 519 | 2047 520 | 2460 521 | 2102 522 | 1745 523 | 2650 524 | 2046 525 | 2514 526 | 1980 527 | 2352 528 | 2113 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 620 | 2297 621 | 2592 622 | 2667 623 | 2703 624 | 2511 625 | 1779 626 | 1782 627 | 2625 628 | 2365 629 | 2315 630 | 2381 631 | 1788 632 | 1714 633 | 2302 634 | 1927 635 | 2325 636 | 2506 637 | 2169 638 | 2328 639 | 2629 640 | 2128 641 | 2655 642 | 2282 643 | 2073 644 | 2395 645 | 2247 646 | 2521 647 | 2260 648 | 1868 649 | 1988 650 | 2324 651 | 2705 652 | 2541 653 | 1731 654 | 2681 655 | 2707 656 | 2465 657 | 1785 658 | 2149 659 | 2045 660 | 2505 661 | 2611 662 | 2217 663 | 2180 664 | 1904 665 | 2453 666 | 2484 667 | 1871 668 | 2309 669 | 2349 670 | 2482 671 | 2004 672 | 1965 673 | 2406 674 | 2162 675 | 1805 676 | 2654 677 | 2007 678 | 1947 679 | 1981 680 | 2112 681 | 2141 682 | 1720 683 | 1758 684 | 2080 685 | 2330 686 | 2030 687 | 2432 688 | 2089 689 | 2547 690 | 1820 691 | 1815 692 | 2675 693 | 1840 694 | 2658 695 | 2370 696 | 2251 697 | 1908 698 | 2029 699 | 2068 700 | 2513 701 | 2549 702 | 2267 703 | 2580 704 | 2327 705 | 2351 706 | 2111 707 | 2022 708 | 2321 709 | 2614 710 | 2252 711 | 2104 712 | 1822 713 | 2552 714 | 2243 715 | 1798 716 | 2396 717 | 2663 718 | 2564 719 | 2148 720 | 2562 721 | 2684 722 | 2001 723 | 2151 724 | 2706 725 | 2240 726 | 2474 727 | 2303 728 | 2634 729 | 2680 730 | 2055 731 | 2090 732 | 2503 733 | 2347 734 | 2402 735 | 2238 736 | 1950 737 | 2054 738 | 2016 739 | 1872 740 | 2233 741 | 1710 742 | 2032 743 | 2540 744 | 2628 745 | 1795 746 | 2616 747 | 1903 748 | 2531 749 | 2567 750 | 1946 751 | 1897 752 | 2222 753 | 2227 754 | 2627 755 | 1856 756 | 2464 757 | 2241 758 | 2481 759 | 2130 760 | 2311 761 | 2083 762 | 2223 763 | 2284 764 | 2235 765 | 2097 766 | 1752 767 | 2515 768 | 2527 769 | 2385 770 | 2189 771 | 2283 772 | 2182 773 | 2079 774 | 2375 775 | 2174 776 | 2437 777 | 1993 778 | 2517 779 | 2443 780 | 2224 781 | 2648 782 | 2171 783 | 2290 784 | 2542 785 | 2038 786 | 1855 787 | 1831 788 | 1759 789 | 1848 790 | 2445 791 | 1827 792 | 2429 793 | 2205 794 | 2598 795 | 2657 796 | 1728 797 | 2065 798 | 1918 799 | 2427 800 | 2573 801 | 2620 802 | 2292 803 | 1777 804 | 2008 805 | 1875 806 | 2288 807 | 2256 808 | 2033 809 | 2470 810 | 2585 811 | 2610 812 | 2082 813 | 2230 814 | 1915 815 | 1847 816 | 2337 817 | 2512 818 | 2386 819 | 2006 820 | 2653 821 | 2346 822 | 1951 823 | 2110 824 | 2639 825 | 2520 826 | 1939 827 | 2683 828 | 2139 829 | 2220 830 | 1910 831 | 2237 832 | 1900 833 | 1836 834 | 2197 835 | 1716 836 | 1860 837 | 2077 838 | 2519 839 | 2538 840 | 2323 841 | 1914 842 | 1971 843 | 1845 844 | 2132 845 | 1802 846 | 1907 847 | 2640 848 | 2496 849 | 2281 850 | 2198 851 | 2416 852 | 2285 853 | 1755 854 | 2431 855 | 2071 856 | 2249 857 | 2123 858 | 1727 859 | 2459 860 | 2304 861 | 2199 862 | 1791 863 | 1809 864 | 1780 865 | 2210 866 | 2417 867 | 1874 868 | 1878 869 | 2116 870 | 1961 871 | 1863 872 | 2579 873 | 2477 874 | 2228 875 | 2332 876 | 2578 877 | 2457 878 | 2024 879 | 1934 880 | 2316 881 | 1841 882 | 1764 883 | 1737 884 | 2322 885 | 2239 886 | 2294 887 | 1729 888 | 2488 889 | 1974 890 | 2473 891 | 2098 892 | 2612 893 | 1834 894 | 2340 895 | 2423 896 | 2175 897 | 2280 898 | 2617 899 | 2208 900 | 2560 901 | 1741 902 | 2600 903 | 2059 904 | 1747 905 | 2242 906 | 2700 907 | 2232 908 | 2057 909 | 2147 910 | 2682 911 | 1792 912 | 1826 913 | 2120 914 | 1895 915 | 2364 916 | 2163 917 | 1851 918 | 2391 919 | 2414 920 | 2452 921 | 1803 922 | 1989 923 | 2623 924 | 2200 925 | 2528 926 | 2415 927 | 1804 928 | 2146 929 | 2619 930 | 2687 931 | 1762 932 | 2172 933 | 2270 934 | 2678 935 | 2593 936 | 2448 937 | 1882 938 | 2257 939 | 2500 940 | 1899 941 | 2478 942 | 2412 943 | 2107 944 | 1746 945 | 2428 946 | 2115 947 | 1800 948 | 1901 949 | 2397 950 | 2530 951 | 1912 952 | 2108 953 | 2206 954 | 2091 955 | 1740 956 | 2219 957 | 1976 958 | 2099 959 | 2142 960 | 2671 961 | 2668 962 | 2216 963 | 2272 964 | 2229 965 | 2666 966 | 2456 967 | 2534 968 | 2697 969 | 2688 970 | 2062 971 | 2691 972 | 2689 973 | 2154 974 | 2590 975 | 2626 976 | 2390 977 | 1813 978 | 2067 979 | 1952 980 | 2518 981 | 2358 982 | 1789 983 | 2076 984 | 2049 985 | 2119 986 | 2013 987 | 2124 988 | 2556 989 | 2105 990 | 2093 991 | 1885 992 | 2305 993 | 2354 994 | 2135 995 | 2601 996 | 1770 997 | 1995 998 | 2504 999 | 1749 1000 | 2157 1001 | -------------------------------------------------------------------------------- /ARGA/arga/data/ind.cora.tx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.cora.tx -------------------------------------------------------------------------------- /ARGA/arga/data/ind.cora.ty: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.cora.ty -------------------------------------------------------------------------------- /ARGA/arga/data/ind.cora.x: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.cora.x -------------------------------------------------------------------------------- /ARGA/arga/data/ind.cora.y: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.cora.y -------------------------------------------------------------------------------- /ARGA/arga/data/ind.pubmed.allx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.pubmed.allx -------------------------------------------------------------------------------- /ARGA/arga/data/ind.pubmed.ally: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.pubmed.ally -------------------------------------------------------------------------------- /ARGA/arga/data/ind.pubmed.graph: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.pubmed.graph -------------------------------------------------------------------------------- /ARGA/arga/data/ind.pubmed.test.index: -------------------------------------------------------------------------------- 1 | 18747 2 | 19392 3 | 19181 4 | 18843 5 | 19221 6 | 18962 7 | 19560 8 | 19097 9 | 18966 10 | 19014 11 | 18756 12 | 19313 13 | 19000 14 | 19569 15 | 19359 16 | 18854 17 | 18970 18 | 19073 19 | 19661 20 | 19180 21 | 19377 22 | 18750 23 | 19401 24 | 18788 25 | 19224 26 | 19447 27 | 19017 28 | 19241 29 | 18890 30 | 18908 31 | 18965 32 | 19001 33 | 18849 34 | 19641 35 | 18852 36 | 19222 37 | 19172 38 | 18762 39 | 19156 40 | 19162 41 | 18856 42 | 18763 43 | 19318 44 | 18826 45 | 19712 46 | 19192 47 | 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 | 18829 219 | 18984 220 | 18931 221 | 18742 222 | 19320 223 | 19111 224 | 19196 225 | 18887 226 | 18991 227 | 19469 228 | 18990 229 | 18876 230 | 19261 231 | 19270 232 | 19522 233 | 19088 234 | 19284 235 | 19646 236 | 19493 237 | 19225 238 | 19615 239 | 19449 240 | 19043 241 | 19674 242 | 19391 243 | 18918 244 | 19155 245 | 19110 246 | 18815 247 | 19131 248 | 18834 249 | 19715 250 | 19603 251 | 19688 252 | 19133 253 | 19053 254 | 19166 255 | 19066 256 | 18893 257 | 18757 258 | 19582 259 | 19282 260 | 19257 261 | 18869 262 | 19467 263 | 18954 264 | 19371 265 | 19151 266 | 19462 267 | 19598 268 | 19653 269 | 19187 270 | 19624 271 | 19564 272 | 19534 273 | 19581 274 | 19478 275 | 18985 276 | 18746 277 | 19342 278 | 18777 279 | 19696 280 | 18824 281 | 19138 282 | 18728 283 | 19643 284 | 19199 285 | 18731 286 | 19168 287 | 18948 288 | 19216 289 | 19697 290 | 19347 291 | 18808 292 | 18725 293 | 19134 294 | 18847 295 | 18828 296 | 18996 297 | 19106 298 | 19485 299 | 18917 300 | 18911 301 | 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 | 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19003 947 | 19026 948 | 19013 949 | 19149 950 | 19177 951 | 19217 952 | 18987 953 | 19354 954 | 19525 955 | 19202 956 | 19084 957 | 19032 958 | 18749 959 | 18867 960 | 19048 961 | 18999 962 | 19260 963 | 19630 964 | 18727 965 | 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 | -------------------------------------------------------------------------------- /ARGA/arga/data/ind.pubmed.tx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.pubmed.tx -------------------------------------------------------------------------------- /ARGA/arga/data/ind.pubmed.ty: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.pubmed.ty -------------------------------------------------------------------------------- /ARGA/arga/data/ind.pubmed.x: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.pubmed.x -------------------------------------------------------------------------------- /ARGA/arga/data/ind.pubmed.y: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA/arga/data/ind.pubmed.y -------------------------------------------------------------------------------- /ARGA/arga/initializations.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | 4 | def weight_variable_glorot(input_dim, output_dim, name=""): 5 | """Create a weight variable with Glorot & Bengio (AISTATS 2010) 6 | initialization. 7 | """ 8 | init_range = np.sqrt(6.0 / (input_dim + output_dim)) 9 | initial = tf.random_uniform([input_dim, output_dim], minval=-init_range, 10 | maxval=init_range, dtype=tf.float32) 11 | return tf.Variable(initial, name=name) 12 | -------------------------------------------------------------------------------- /ARGA/arga/input_data.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pickle as pkl 3 | import networkx as nx 4 | import scipy.sparse as sp 5 | 6 | 7 | def parse_index_file(filename): 8 | index = [] 9 | for line in open(filename): 10 | index.append(int(line.strip())) 11 | return index 12 | 13 | def sample_mask(idx, l): 14 | """Create mask.""" 15 | mask = np.zeros(l) 16 | mask[idx] = 1 17 | return np.array(mask, dtype=np.bool) 18 | 19 | def load_data(dataset): 20 | # load the data: x, tx, allx, graph 21 | names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph'] 22 | objects = [] 23 | for i in range(len(names)): 24 | objects.append(pkl.load(open("data/ind.{}.{}".format(dataset, names[i])))) 25 | x, y, tx, ty, allx, ally, graph = tuple(objects) 26 | test_idx_reorder = parse_index_file("data/ind.{}.test.index".format(dataset)) 27 | test_idx_range = np.sort(test_idx_reorder) 28 | 29 | if dataset == 'citeseer': 30 | # Fix citeseer dataset (there are some isolated nodes in the graph) 31 | # Find isolated nodes, add them as zero-vecs into the right position 32 | test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1) 33 | tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1])) 34 | tx_extended[test_idx_range-min(test_idx_range), :] = tx 35 | tx = tx_extended 36 | ty_extended = np.zeros((len(test_idx_range_full), y.shape[1])) 37 | ty_extended[test_idx_range - min(test_idx_range), :] = ty 38 | ty = ty_extended 39 | 40 | features = sp.vstack((allx, tx)).tolil() 41 | features[test_idx_reorder, :] = features[test_idx_range, :] 42 | adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph)) 43 | 44 | labels = np.vstack((ally, ty)) 45 | labels[test_idx_reorder, :] = labels[test_idx_range, :] 46 | 47 | idx_test = test_idx_range.tolist() 48 | idx_train = range(len(y)) 49 | idx_val = range(len(y), len(y) + 500) 50 | 51 | train_mask = sample_mask(idx_train, labels.shape[0]) 52 | val_mask = sample_mask(idx_val, labels.shape[0]) 53 | test_mask = sample_mask(idx_test, labels.shape[0]) 54 | 55 | y_train = np.zeros(labels.shape) 56 | y_val = np.zeros(labels.shape) 57 | y_test = np.zeros(labels.shape) 58 | y_train[train_mask, :] = labels[train_mask, :] 59 | y_val[val_mask, :] = labels[val_mask, :] 60 | y_test[test_mask, :] = labels[test_mask, :] 61 | 62 | return adj, features, y_test, tx, ty, test_mask, np.argmax(labels,1) 63 | 64 | 65 | def load_alldata(dataset_str): 66 | """Load data.""" 67 | names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph'] 68 | objects = [] 69 | for i in range(len(names)): 70 | objects.append(pkl.load(open("data/ind.{}.{}".format(dataset_str, names[i])))) 71 | 72 | x, y, tx, ty, allx, ally, graph = tuple(objects) 73 | test_idx_reorder = parse_index_file("data/ind.{}.test.index".format(dataset_str)) 74 | test_idx_range = np.sort(test_idx_reorder) 75 | 76 | if dataset_str == 'citeseer': 77 | # Fix citeseer dataset (there are some isolated nodes in the graph) 78 | # Find isolated nodes, add them as zero-vecs into the right position 79 | test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1) 80 | tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1])) 81 | tx_extended[test_idx_range-min(test_idx_range), :] = tx 82 | tx = tx_extended 83 | ty_extended = np.zeros((len(test_idx_range_full), y.shape[1])) 84 | ty_extended[test_idx_range-min(test_idx_range), :] = ty 85 | ty = ty_extended 86 | 87 | features = sp.vstack((allx, tx)).tolil() 88 | features[test_idx_reorder, :] = features[test_idx_range, :] 89 | adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph)) 90 | 91 | labels = np.vstack((ally, ty)) 92 | labels[test_idx_reorder, :] = labels[test_idx_range, :] 93 | 94 | idx_test = test_idx_range.tolist() 95 | idx_train = range(len(y)) 96 | idx_val = range(len(y), len(y)+500) 97 | 98 | train_mask = sample_mask(idx_train, labels.shape[0]) 99 | val_mask = sample_mask(idx_val, labels.shape[0]) 100 | test_mask = sample_mask(idx_test, labels.shape[0]) 101 | 102 | y_train = np.zeros(labels.shape) 103 | y_val = np.zeros(labels.shape) 104 | y_test = np.zeros(labels.shape) 105 | y_train[train_mask, :] = labels[train_mask, :] 106 | y_val[val_mask, :] = labels[val_mask, :] 107 | y_test[test_mask, :] = labels[test_mask, :] 108 | 109 | return adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, np.argmax(labels, 1) 110 | -------------------------------------------------------------------------------- /ARGA/arga/layers.py: -------------------------------------------------------------------------------- 1 | from initializations 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 | """ 14 | if layer_name not in _LAYER_UIDS: 15 | _LAYER_UIDS[layer_name] = 1 16 | return 1 17 | else: 18 | _LAYER_UIDS[layer_name] += 1 19 | return _LAYER_UIDS[layer_name] 20 | 21 | 22 | def dropout_sparse(x, keep_prob, num_nonzero_elems): 23 | """Dropout for sparse tensors. Currently fails for very large sparse tensors (>1M elements) 24 | """ 25 | noise_shape = [num_nonzero_elems] 26 | random_tensor = keep_prob 27 | random_tensor += tf.random_uniform(noise_shape) 28 | dropout_mask = tf.cast(tf.floor(random_tensor), dtype=tf.bool) 29 | pre_out = tf.sparse_retain(x, dropout_mask) 30 | return pre_out * (1./keep_prob) 31 | 32 | 33 | class Layer(object): 34 | """Base layer class. Defines basic API for all layer objects. 35 | 36 | # Properties 37 | name: String, defines the variable scope of the layer. 38 | 39 | # Methods 40 | _call(inputs): Defines computation graph of layer 41 | (i.e. takes input, returns output) 42 | __call__(inputs): Wrapper for _call() 43 | """ 44 | def __init__(self, **kwargs): 45 | allowed_kwargs = {'name', 'logging'} 46 | for kwarg in kwargs.keys(): 47 | assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg 48 | name = kwargs.get('name') 49 | if not name: 50 | layer = self.__class__.__name__.lower() 51 | name = layer + '_' + str(get_layer_uid(layer)) 52 | self.name = name 53 | self.vars = {} 54 | logging = kwargs.get('logging', False) 55 | self.logging = logging 56 | self.issparse = False 57 | 58 | def _call(self, inputs): 59 | return inputs 60 | 61 | def __call__(self, inputs): 62 | with tf.name_scope(self.name): 63 | outputs = self._call(inputs) 64 | return outputs 65 | 66 | 67 | class GraphConvolution(Layer): 68 | """Basic graph convolution layer for undirected graph without edge labels.""" 69 | def __init__(self, input_dim, output_dim, adj, dropout=0., act=tf.nn.relu, **kwargs): 70 | super(GraphConvolution, self).__init__(**kwargs) 71 | with tf.variable_scope(self.name + '_vars'): 72 | self.vars['weights'] = weight_variable_glorot(input_dim, output_dim, name="weights") 73 | self.dropout = dropout 74 | self.adj = adj 75 | self.act = act 76 | 77 | def _call(self, inputs): 78 | x = inputs 79 | x = tf.nn.dropout(x, 1-self.dropout) 80 | x = tf.matmul(x, self.vars['weights']) 81 | x = tf.sparse_tensor_dense_matmul(self.adj, x) 82 | outputs = self.act(x) 83 | return outputs 84 | 85 | 86 | class GraphConvolutionSparse(Layer): 87 | """Graph convolution layer for sparse inputs.""" 88 | def __init__(self, input_dim, output_dim, adj, features_nonzero, dropout=0., act=tf.nn.relu, **kwargs): 89 | super(GraphConvolutionSparse, self).__init__(**kwargs) 90 | with tf.variable_scope(self.name + '_vars'): 91 | self.vars['weights'] = weight_variable_glorot(input_dim, output_dim, name="weights") 92 | self.dropout = dropout 93 | self.adj = adj 94 | self.act = act 95 | self.issparse = True 96 | self.features_nonzero = features_nonzero 97 | 98 | def _call(self, inputs): 99 | x = inputs 100 | x = dropout_sparse(x, 1-self.dropout, self.features_nonzero) 101 | x = tf.sparse_tensor_dense_matmul(x, self.vars['weights']) 102 | x = tf.sparse_tensor_dense_matmul(self.adj, x) 103 | outputs = self.act(x) 104 | return outputs 105 | 106 | 107 | class InnerProductDecoder(Layer): 108 | """Decoder model layer for link prediction.""" 109 | def __init__(self, input_dim, dropout=0., act=tf.nn.sigmoid, **kwargs): 110 | super(InnerProductDecoder, self).__init__(**kwargs) 111 | self.dropout = dropout 112 | self.act = act 113 | 114 | def _call(self, inputs): 115 | inputs = tf.nn.dropout(inputs, 1-self.dropout) 116 | x = tf.transpose(inputs) 117 | x = tf.matmul(inputs, x) 118 | x = tf.reshape(x, [-1]) 119 | outputs = self.act(x) 120 | return outputs 121 | 122 | 123 | -------------------------------------------------------------------------------- /ARGA/arga/link_prediction.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | from __future__ import print_function 3 | import os 4 | # Train on CPU (hide GPU) due to memory constraints 5 | os.environ['CUDA_VISIBLE_DEVICES'] = "" 6 | 7 | import tensorflow as tf 8 | import settings 9 | from constructor import get_placeholder, get_model, format_data, get_optimizer, update 10 | from metrics import linkpred_metrics 11 | # Settings 12 | flags = tf.app.flags 13 | FLAGS = flags.FLAGS 14 | 15 | class Link_pred_Runner(): 16 | def __init__(self, settings): 17 | self.data_name = settings['data_name'] 18 | self.iteration = settings['iterations'] 19 | self.model = settings['model'] 20 | 21 | def erun(self): 22 | model_str = self.model 23 | # formatted data 24 | feas = format_data(self.data_name) 25 | 26 | # Define placeholders 27 | placeholders = get_placeholder(feas['adj']) 28 | 29 | # construct model 30 | d_real, discriminator, ae_model = get_model(model_str, placeholders, feas['num_features'], feas['num_nodes'], feas['features_nonzero']) 31 | 32 | # Optimizer 33 | opt = get_optimizer(model_str, ae_model, discriminator, placeholders, feas['pos_weight'], feas['norm'], d_real, feas['num_nodes']) 34 | 35 | # Initialize session 36 | sess = tf.Session() 37 | sess.run(tf.global_variables_initializer()) 38 | 39 | val_roc_score = [] 40 | 41 | # Train model 42 | for epoch in range(self.iteration): 43 | 44 | emb, avg_cost = update(ae_model, opt, sess, feas['adj_norm'], feas['adj_label'], feas['features'], placeholders, feas['adj']) 45 | 46 | lm_train = linkpred_metrics(feas['val_edges'], feas['val_edges_false']) 47 | roc_curr, ap_curr, _ = lm_train.get_roc_score(emb, feas) 48 | val_roc_score.append(roc_curr) 49 | 50 | print("Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(avg_cost), "val_roc=", "{:.5f}".format(val_roc_score[-1]), "val_ap=", "{:.5f}".format(ap_curr)) 51 | 52 | if (epoch+1) % 10 == 0: 53 | lm_test = linkpred_metrics(feas['test_edges'], feas['test_edges_false']) 54 | roc_score, ap_score,_ = lm_test.get_roc_score(emb, feas) 55 | print('Test ROC score: ' + str(roc_score)) 56 | print('Test AP score: ' + str(ap_score)) -------------------------------------------------------------------------------- /ARGA/arga/metrics.py: -------------------------------------------------------------------------------- 1 | from sklearn.metrics import f1_score 2 | from sklearn.metrics import roc_auc_score 3 | from sklearn.metrics import average_precision_score 4 | from sklearn import metrics 5 | from munkres import Munkres, print_matrix 6 | import numpy as np 7 | 8 | class linkpred_metrics(): 9 | def __init__(self, edges_pos, edges_neg): 10 | self.edges_pos = edges_pos 11 | self.edges_neg = edges_neg 12 | 13 | def get_roc_score(self, emb, feas): 14 | # if emb is None: 15 | # feed_dict.update({placeholders['dropout']: 0}) 16 | # emb = sess.run(model.z_mean, feed_dict=feed_dict) 17 | 18 | def sigmoid(x): 19 | return 1 / (1 + np.exp(-x)) 20 | 21 | # Predict on test set of edges 22 | adj_rec = np.dot(emb, emb.T) 23 | preds = [] 24 | pos = [] 25 | for e in self.edges_pos: 26 | preds.append(sigmoid(adj_rec[e[0], e[1]])) 27 | pos.append(feas['adj_orig'][e[0], e[1]]) 28 | 29 | preds_neg = [] 30 | neg = [] 31 | for e in self.edges_neg: 32 | preds_neg.append(sigmoid(adj_rec[e[0], e[1]])) 33 | neg.append(feas['adj_orig'][e[0], e[1]]) 34 | 35 | preds_all = np.hstack([preds, preds_neg]) 36 | labels_all = np.hstack([np.ones(len(preds)), np.zeros(len(preds))]) 37 | roc_score = roc_auc_score(labels_all, preds_all) 38 | ap_score = average_precision_score(labels_all, preds_all) 39 | 40 | return roc_score, ap_score, emb 41 | 42 | 43 | class clustering_metrics(): 44 | def __init__(self, true_label, predict_label): 45 | self.true_label = true_label 46 | self.pred_label = predict_label 47 | 48 | 49 | def clusteringAcc(self): 50 | # best mapping between true_label and predict label 51 | l1 = list(set(self.true_label)) 52 | numclass1 = len(l1) 53 | 54 | l2 = list(set(self.pred_label)) 55 | numclass2 = len(l2) 56 | if numclass1 != numclass2: 57 | print('Class Not equal, Error!!!!') 58 | return 0 59 | 60 | cost = np.zeros((numclass1, numclass2), dtype=int) 61 | for i, c1 in enumerate(l1): 62 | mps = [i1 for i1, e1 in enumerate(self.true_label) if e1 == c1] 63 | for j, c2 in enumerate(l2): 64 | mps_d = [i1 for i1 in mps if self.pred_label[i1] == c2] 65 | 66 | cost[i][j] = len(mps_d) 67 | 68 | # match two clustering results by Munkres algorithm 69 | m = Munkres() 70 | cost = cost.__neg__().tolist() 71 | 72 | indexes = m.compute(cost) 73 | 74 | # get the match results 75 | new_predict = np.zeros(len(self.pred_label)) 76 | for i, c in enumerate(l1): 77 | # correponding label in l2: 78 | c2 = l2[indexes[i][1]] 79 | 80 | # ai is the index with label==c2 in the pred_label list 81 | ai = [ind for ind, elm in enumerate(self.pred_label) if elm == c2] 82 | new_predict[ai] = c 83 | 84 | acc = metrics.accuracy_score(self.true_label, new_predict) 85 | f1_macro = metrics.f1_score(self.true_label, new_predict, average='macro') 86 | precision_macro = metrics.precision_score(self.true_label, new_predict, average='macro') 87 | recall_macro = metrics.recall_score(self.true_label, new_predict, average='macro') 88 | f1_micro = metrics.f1_score(self.true_label, new_predict, average='micro') 89 | precision_micro = metrics.precision_score(self.true_label, new_predict, average='micro') 90 | recall_micro = metrics.recall_score(self.true_label, new_predict, average='micro') 91 | return acc, f1_macro, precision_macro, recall_macro, f1_micro, precision_micro, recall_micro 92 | 93 | def evaluationClusterModelFromLabel(self): 94 | nmi = metrics.normalized_mutual_info_score(self.true_label, self.pred_label) 95 | adjscore = metrics.adjusted_rand_score(self.true_label, self.pred_label) 96 | acc, f1_macro, precision_macro, recall_macro, f1_micro, precision_micro, recall_micro = self.clusteringAcc() 97 | 98 | print('ACC=%f, f1_macro=%f, precision_macro=%f, recall_macro=%f, f1_micro=%f, precision_micro=%f, recall_micro=%f, NMI=%f, ADJ_RAND_SCORE=%f' % (acc, f1_macro, precision_macro, recall_macro, f1_micro, precision_micro, recall_micro, nmi, adjscore)) 99 | 100 | fh = open('recoder.txt', 'a') 101 | 102 | fh.write('ACC=%f, f1_macro=%f, precision_macro=%f, recall_macro=%f, f1_micro=%f, precision_micro=%f, recall_micro=%f, NMI=%f, ADJ_RAND_SCORE=%f' % (acc, f1_macro, precision_macro, recall_macro, f1_micro, precision_micro, recall_micro, nmi, adjscore) ) 103 | fh.write('\r\n') 104 | fh.flush() 105 | fh.close() 106 | 107 | return acc, nmi, adjscore 108 | 109 | -------------------------------------------------------------------------------- /ARGA/arga/model.py: -------------------------------------------------------------------------------- 1 | from layers import GraphConvolution, GraphConvolutionSparse, InnerProductDecoder 2 | import tensorflow as tf 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 | 14 | for kwarg in kwargs.keys(): 15 | assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg 16 | name = kwargs.get('name') 17 | if not name: 18 | name = self.__class__.__name__.lower() 19 | self.name = name 20 | 21 | logging = kwargs.get('logging', False) 22 | self.logging = logging 23 | 24 | self.vars = {} 25 | 26 | def _build(self): 27 | raise NotImplementedError 28 | 29 | def build(self): 30 | """ Wrapper for _build() """ 31 | with tf.variable_scope(self.name): 32 | self._build() 33 | variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name) 34 | self.vars = {var.name: var for var in variables} 35 | 36 | def fit(self): 37 | pass 38 | 39 | def predict(self): 40 | pass 41 | 42 | 43 | class ARGA(Model): 44 | def __init__(self, placeholders, num_features, features_nonzero, **kwargs): 45 | super(ARGA, self).__init__(**kwargs) 46 | 47 | self.inputs = placeholders['features'] 48 | self.input_dim = num_features 49 | self.features_nonzero = features_nonzero 50 | self.adj = placeholders['adj'] 51 | self.dropout = placeholders['dropout'] 52 | self.build() 53 | 54 | def _build(self): 55 | 56 | with tf.variable_scope('Encoder', reuse=None): 57 | self.hidden1 = GraphConvolutionSparse(input_dim=self.input_dim, 58 | output_dim=FLAGS.hidden1, 59 | adj=self.adj, 60 | features_nonzero=self.features_nonzero, 61 | act=tf.nn.relu, 62 | dropout=self.dropout, 63 | logging=self.logging, 64 | name='e_dense_1')(self.inputs) 65 | 66 | 67 | self.noise = gaussian_noise_layer(self.hidden1, 0.1) 68 | 69 | self.embeddings = GraphConvolution(input_dim=FLAGS.hidden1, 70 | output_dim=FLAGS.hidden2, 71 | adj=self.adj, 72 | act=lambda x: x, 73 | dropout=self.dropout, 74 | logging=self.logging, 75 | name='e_dense_2')(self.noise) 76 | 77 | 78 | self.z_mean = self.embeddings 79 | 80 | self.reconstructions = InnerProductDecoder(input_dim=FLAGS.hidden2, 81 | act=lambda x: x, 82 | logging=self.logging)(self.embeddings) 83 | 84 | 85 | 86 | 87 | class ARVGA(Model): 88 | def __init__(self, placeholders, num_features, num_nodes, features_nonzero, **kwargs): 89 | super(ARVGA, self).__init__(**kwargs) 90 | 91 | self.inputs = placeholders['features'] 92 | self.input_dim = num_features 93 | self.features_nonzero = features_nonzero 94 | self.n_samples = num_nodes 95 | self.adj = placeholders['adj'] 96 | self.dropout = placeholders['dropout'] 97 | self.build() 98 | 99 | def _build(self): 100 | with tf.variable_scope('Encoder'): 101 | self.hidden1 = GraphConvolutionSparse(input_dim=self.input_dim, 102 | output_dim=FLAGS.hidden1, 103 | adj=self.adj, 104 | features_nonzero=self.features_nonzero, 105 | act=tf.nn.relu, 106 | dropout=self.dropout, 107 | logging=self.logging, 108 | name='e_dense_1')(self.inputs) 109 | 110 | self.z_mean = GraphConvolution(input_dim=FLAGS.hidden1, 111 | output_dim=FLAGS.hidden2, 112 | adj=self.adj, 113 | act=lambda x: x, 114 | dropout=self.dropout, 115 | logging=self.logging, 116 | name='e_dense_2')(self.hidden1) 117 | 118 | self.z_log_std = GraphConvolution(input_dim=FLAGS.hidden1, 119 | output_dim=FLAGS.hidden2, 120 | adj=self.adj, 121 | act=lambda x: x, 122 | dropout=self.dropout, 123 | logging=self.logging, 124 | name='e_dense_3')(self.hidden1) 125 | 126 | self.z = self.z_mean + tf.random_normal([self.n_samples, FLAGS.hidden2]) * tf.exp(self.z_log_std) 127 | 128 | self.reconstructions = InnerProductDecoder(input_dim=FLAGS.hidden2, 129 | act=lambda x: x, 130 | logging=self.logging)(self.z) 131 | self.embeddings = self.z 132 | 133 | 134 | def dense(x, n1, n2, name): 135 | """ 136 | Used to create a dense layer. 137 | :param x: input tensor to the dense layer 138 | :param n1: no. of input neurons 139 | :param n2: no. of output neurons 140 | :param name: name of the entire dense layer.i.e, variable scope name. 141 | :return: tensor with shape [batch_size, n2] 142 | """ 143 | with tf.variable_scope(name, reuse=None): 144 | # np.random.seed(1) 145 | tf.set_random_seed(1) 146 | weights = tf.get_variable("weights", shape=[n1, n2], 147 | initializer=tf.random_normal_initializer(mean=0., stddev=0.01)) 148 | bias = tf.get_variable("bias", shape=[n2], initializer=tf.constant_initializer(0.0)) 149 | out = tf.add(tf.matmul(x, weights), bias, name='matmul') 150 | return out 151 | 152 | 153 | class Discriminator(Model): 154 | def __init__(self, **kwargs): 155 | super(Discriminator, self).__init__(**kwargs) 156 | 157 | self.act = tf.nn.relu 158 | 159 | def construct(self, inputs, reuse = False): 160 | # with tf.name_scope('Discriminator'): 161 | with tf.variable_scope('Discriminator'): 162 | if reuse: 163 | tf.get_variable_scope().reuse_variables() 164 | # np.random.seed(1) 165 | tf.set_random_seed(1) 166 | dc_den1 = tf.nn.relu(dense(inputs, FLAGS.hidden2, FLAGS.hidden3, name='dc_den1')) 167 | dc_den2 = tf.nn.relu(dense(dc_den1, FLAGS.hidden3, FLAGS.hidden1, name='dc_den2')) 168 | output = dense(dc_den2, FLAGS.hidden1, 1, name='dc_output') 169 | return output 170 | 171 | def gaussian_noise_layer(input_layer, std): 172 | noise = tf.random_normal(shape=tf.shape(input_layer), mean=0.0, stddev=std, dtype=tf.float32) 173 | return input_layer + noise 174 | -------------------------------------------------------------------------------- /ARGA/arga/optimizer.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | 3 | flags = tf.app.flags 4 | FLAGS = flags.FLAGS 5 | 6 | 7 | class OptimizerAE(object): 8 | def __init__(self, preds, labels, pos_weight, norm, d_real, d_fake): 9 | preds_sub = preds 10 | labels_sub = labels 11 | 12 | self.real = d_real 13 | 14 | # Discrimminator Loss 15 | self.dc_loss_real = tf.reduce_mean( 16 | tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(self.real), logits=self.real,name='dclreal')) 17 | 18 | self.dc_loss_fake = tf.reduce_mean( 19 | tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_fake), logits=d_fake,name='dcfake')) 20 | self.dc_loss = self.dc_loss_fake + self.dc_loss_real 21 | 22 | # Generator loss 23 | generator_loss = tf.reduce_mean( 24 | tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_fake), logits=d_fake, name='gl')) 25 | 26 | 27 | 28 | self.cost = norm * tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(logits=preds_sub, targets=labels_sub, pos_weight=pos_weight)) 29 | self.generator_loss = generator_loss + self.cost 30 | self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) # Adam Optimizer 31 | 32 | all_variables = tf.trainable_variables() 33 | dc_var = [var for var in all_variables if 'dc_' in var.name] 34 | en_var = [var for var in all_variables if 'e_' in var.name] 35 | 36 | 37 | with tf.variable_scope(tf.get_variable_scope()): 38 | self.discriminator_optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.discriminator_learning_rate, 39 | beta1=0.9, name='adam1').minimize(self.dc_loss, var_list=dc_var) #minimize(dc_loss_real, var_list=dc_var) 40 | 41 | self.generator_optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.discriminator_learning_rate, 42 | beta1=0.9, name='adam2').minimize(self.generator_loss, var_list=en_var) 43 | 44 | 45 | 46 | self.opt_op = self.optimizer.minimize(self.cost) 47 | self.grads_vars = self.optimizer.compute_gradients(self.cost) 48 | 49 | 50 | class OptimizerVAE(object): 51 | def __init__(self, preds, labels, model, num_nodes, pos_weight, norm, d_real, d_fake): 52 | preds_sub = preds 53 | labels_sub = labels 54 | 55 | # Discrimminator Loss 56 | dc_loss_real = tf.reduce_mean( 57 | tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_real), logits=d_real)) 58 | dc_loss_fake = tf.reduce_mean( 59 | tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_fake), logits=d_fake)) 60 | self.dc_loss = dc_loss_fake + dc_loss_real 61 | 62 | # Generator loss 63 | self.generator_loss = tf.reduce_mean( 64 | tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_fake), logits=d_fake)) 65 | 66 | self.cost = norm * tf.reduce_mean( 67 | tf.nn.weighted_cross_entropy_with_logits(logits=preds_sub, targets=labels_sub, pos_weight=pos_weight)) 68 | self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) # Adam Optimizer 69 | 70 | all_variables = tf.trainable_variables() 71 | dc_var = [var for var in all_variables if 'dc_' in var.op.name] 72 | en_var = [var for var in all_variables if 'e_' in var.op.name] 73 | 74 | 75 | with tf.variable_scope(tf.get_variable_scope(), reuse=False): 76 | self.discriminator_optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.discriminator_learning_rate, 77 | beta1=0.9, name='adam1').minimize(self.dc_loss, var_list=dc_var)#minimize(dc_loss_real, var_list=dc_var) 78 | 79 | self.generator_optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.discriminator_learning_rate, 80 | beta1=0.9, name='adam2').minimize(self.generator_loss, 81 | var_list=en_var) 82 | 83 | self.cost = norm * tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(logits=preds_sub, targets=labels_sub, pos_weight=pos_weight)) 84 | self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) # Adam Optimizer 85 | 86 | # Latent loss 87 | self.log_lik = self.cost 88 | self.kl = (0.5 / num_nodes) * tf.reduce_mean(tf.reduce_sum(1 + 2 * model.z_log_std - tf.square(model.z_mean) - 89 | tf.square(tf.exp(model.z_log_std)), 1)) 90 | self.cost -= self.kl 91 | 92 | self.opt_op = self.optimizer.minimize(self.cost) 93 | self.grads_vars = self.optimizer.compute_gradients(self.cost) 94 | -------------------------------------------------------------------------------- /ARGA/arga/preprocessing.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import scipy.sparse as sp 3 | 4 | 5 | def sparse_to_tuple(sparse_mx): 6 | if not sp.isspmatrix_coo(sparse_mx): 7 | sparse_mx = sparse_mx.tocoo() 8 | coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose() 9 | values = sparse_mx.data 10 | shape = sparse_mx.shape 11 | return coords, values, shape 12 | 13 | 14 | def preprocess_graph(adj): 15 | adj = sp.coo_matrix(adj) 16 | adj_ = adj + sp.eye(adj.shape[0]) 17 | rowsum = np.array(adj_.sum(1)) 18 | degree_mat_inv_sqrt = sp.diags(np.power(rowsum, -0.5).flatten()) 19 | adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt).tocoo() 20 | return sparse_to_tuple(adj_normalized) 21 | 22 | 23 | def construct_feed_dict(adj_normalized, adj, features, placeholders): 24 | # construct feed dictionary 25 | feed_dict = dict() 26 | feed_dict.update({placeholders['features']: features}) 27 | feed_dict.update({placeholders['adj']: adj_normalized}) 28 | feed_dict.update({placeholders['adj_orig']: adj}) 29 | return feed_dict 30 | 31 | 32 | def mask_test_edges(adj): 33 | # Function to build test set with 10% positive links 34 | # NOTE: Splits are randomized and results might slightly deviate from reported numbers in the paper. 35 | # TODO: Clean up. 36 | 37 | # Remove diagonal elements 38 | adj = adj - sp.dia_matrix((adj.diagonal()[np.newaxis, :], [0]), shape=adj.shape) 39 | adj.eliminate_zeros() 40 | # Check that diag is zero: 41 | assert np.diag(adj.todense()).sum() == 0 42 | 43 | adj_triu = sp.triu(adj) 44 | adj_tuple = sparse_to_tuple(adj_triu) 45 | edges = adj_tuple[0] 46 | edges_all = sparse_to_tuple(adj)[0] 47 | num_test = int(np.floor(edges.shape[0] / 10.)) 48 | num_val = int(np.floor(edges.shape[0] / 20.)) 49 | 50 | all_edge_idx = range(edges.shape[0]) 51 | np.random.shuffle(all_edge_idx) 52 | val_edge_idx = all_edge_idx[:num_val] 53 | test_edge_idx = all_edge_idx[num_val:(num_val + num_test)] 54 | test_edges = edges[test_edge_idx] 55 | val_edges = edges[val_edge_idx] 56 | train_edges = np.delete(edges, np.hstack([test_edge_idx, val_edge_idx]), axis=0) 57 | 58 | def ismember(a, b, tol=5): 59 | rows_close = np.all(np.round(a - b[:, None], tol) == 0, axis=-1) 60 | return (np.all(np.any(rows_close, axis=-1), axis=-1) and 61 | np.all(np.any(rows_close, axis=0), axis=0)) 62 | 63 | test_edges_false = [] 64 | while len(test_edges_false) < len(test_edges): 65 | idx_i = np.random.randint(0, adj.shape[0]) 66 | idx_j = np.random.randint(0, adj.shape[0]) 67 | if idx_i == idx_j: 68 | continue 69 | if ismember([idx_i, idx_j], edges_all): 70 | continue 71 | if test_edges_false: 72 | if ismember([idx_j, idx_i], np.array(test_edges_false)): 73 | continue 74 | if ismember([idx_i, idx_j], np.array(test_edges_false)): 75 | continue 76 | test_edges_false.append([idx_i, idx_j]) 77 | 78 | val_edges_false = [] 79 | while len(val_edges_false) < len(val_edges): 80 | idx_i = np.random.randint(0, adj.shape[0]) 81 | idx_j = np.random.randint(0, adj.shape[0]) 82 | if idx_i == idx_j: 83 | continue 84 | if ismember([idx_i, idx_j], train_edges): 85 | continue 86 | if ismember([idx_j, idx_i], train_edges): 87 | continue 88 | if ismember([idx_i, idx_j], val_edges): 89 | continue 90 | if ismember([idx_j, idx_i], val_edges): 91 | continue 92 | if val_edges_false: 93 | if ismember([idx_j, idx_i], np.array(val_edges_false)): 94 | continue 95 | if ismember([idx_i, idx_j], np.array(val_edges_false)): 96 | continue 97 | val_edges_false.append([idx_i, idx_j]) 98 | 99 | assert ~ismember(test_edges_false, edges_all) 100 | assert ~ismember(val_edges_false, edges_all) 101 | assert ~ismember(val_edges, train_edges) 102 | assert ~ismember(test_edges, train_edges) 103 | assert ~ismember(val_edges, test_edges) 104 | 105 | data = np.ones(train_edges.shape[0]) 106 | 107 | # Re-build adj matrix 108 | adj_train = sp.csr_matrix((data, (train_edges[:, 0], train_edges[:, 1])), shape=adj.shape) 109 | adj_train = adj_train + adj_train.T 110 | 111 | # NOTE: these edge lists only contain single direction of edge! 112 | return adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false 113 | 114 | 115 | -------------------------------------------------------------------------------- /ARGA/arga/run.py: -------------------------------------------------------------------------------- 1 | import settings 2 | 3 | from clustering import Clustering_Runner 4 | from link_prediction import Link_pred_Runner 5 | 6 | 7 | dataname = 'cora' # 'cora' or 'citeseer' or 'pubmed' 8 | model = 'arga_ae' # 'arga_ae' or 'arga_vae' 9 | task = 'link_prediction' # 'clustering' or 'link_prediction' 10 | 11 | settings = settings.get_settings(dataname, model, task) 12 | 13 | if task == 'clustering': 14 | runner = Clustering_Runner(settings) 15 | else: 16 | runner = Link_pred_Runner(settings) 17 | 18 | runner.erun() 19 | 20 | -------------------------------------------------------------------------------- /ARGA/arga/settings.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | flags = tf.app.flags 4 | FLAGS = flags.FLAGS 5 | 6 | 7 | flags.DEFINE_integer('hidden3', 64, 'Number of units in hidden layer 3.') 8 | flags.DEFINE_integer('discriminator_out', 0, 'discriminator_out.') 9 | flags.DEFINE_float('discriminator_learning_rate', 0.001, 'Initial learning rate.') 10 | flags.DEFINE_float('learning_rate', .5*0.001, 'Initial learning rate.') 11 | flags.DEFINE_integer('hidden1', 32, 'Number of units in hidden layer 1.') 12 | flags.DEFINE_integer('hidden2', 32, 'Number of units in hidden layer 2.') 13 | flags.DEFINE_float('weight_decay', 0., 'Weight for L2 loss on embedding matrix.') 14 | flags.DEFINE_float('dropout', 0., 'Dropout rate (1 - keep probability).') 15 | flags.DEFINE_integer('features', 1, 'Whether to use features (1) or not (0).') 16 | flags.DEFINE_integer('seed', 50, 'seed for fixing the results.') 17 | flags.DEFINE_integer('iterations', 50, 'number of iterations.') 18 | 19 | ''' 20 | infor: number of clusters 21 | ''' 22 | infor = {'cora': 7, 'citeseer': 6, 'pubmed':3} 23 | 24 | 25 | ''' 26 | We did not set any seed when we conducted the experiments described in the paper; 27 | We set a seed here to steadily reveal better performance of ARGA 28 | ''' 29 | seed = 7 30 | np.random.seed(seed) 31 | tf.set_random_seed(seed) 32 | 33 | def get_settings(dataname, model, task): 34 | if dataname != 'citeseer' and dataname != 'cora' and dataname != 'pubmed': 35 | print('error: wrong data set name') 36 | if model != 'arga_ae' and model != 'arga_vae': 37 | print('error: wrong model name') 38 | if task != 'clustering' and task != 'link_prediction': 39 | print('error: wrong task name') 40 | 41 | if task == 'clustering': 42 | iterations = FLAGS.iterations 43 | clustering_num = infor[dataname] 44 | re = {'data_name': dataname, 'iterations' : iterations, 'clustering_num' :clustering_num, 'model' : model} 45 | elif task == 'link_prediction': 46 | iterations = 4 * FLAGS.iterations 47 | re = {'data_name': dataname, 'iterations' : iterations,'model' : model} 48 | 49 | return re 50 | 51 | -------------------------------------------------------------------------------- /ARGA_FLOW.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrustAGI-Lab/ARGA/a970fa583d8c474b18f950da06bf91da03a647db/ARGA_FLOW.jpg -------------------------------------------------------------------------------- /LICENCE: -------------------------------------------------------------------------------- 1 | The MIT License 2 | 3 | Copyright (c) 2018 Shirui Pan & Ruiqi Hu 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 | Adversarially Regularized Graph Autoencoder (ARGA) 2 | ============ 3 | 4 | This is a TensorFlow implementation of the Adversarially Regularized Graph Autoencoder(ARGA) model as described in our paper: 5 | 6 | Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., & Zhang, C. (2018). Adversarially Regularized Graph Autoencoder for Graph Embedding, [https://www.ijcai.org/proceedings/2018/0362.pdf], published in IJCAI 2018: 2609-2615. 7 | 8 | ![Construction of ARGA](https://github.com/Ruiqi-Hu/ARGA/blob/master/ARGA_FLOW.jpg) 9 | 10 | We borrowed part of code from T. N. Kipf, M. Welling, Variational Graph Auto-Encoders [https://github.com/tkipf/gae] 11 | 12 | 13 | ## Installation 14 | 15 | ```bash 16 | pip install -r requirements.txt 17 | ``` 18 | 19 | ## Requirements 20 | * TensorFlow (1.0 or later) 21 | * python 2.7 22 | * networkx 23 | * scikit-learn 24 | * scipy 25 | 26 | ## Run from 27 | 28 | ```bash 29 | python run.py 30 | ``` 31 | 32 | ## Data 33 | 34 | In order to use your own data, you have to provide 35 | * an N by N adjacency matrix (N is the number of nodes), and 36 | * an N by D feature matrix (D is the number of features per node) -- optional 37 | 38 | Have a look at the `load_data()` function in `input_data.py` for an example. 39 | 40 | In this example, we load citation network data (Cora, Citeseer or Pubmed). The original datasets can be found here: http://linqs.cs.umd.edu/projects/projects/lbc/ and here (in a different format): https://github.com/kimiyoung/planetoid 41 | 42 | ## Models 43 | 44 | You can choose between the following models: 45 | * `arga_ae`: Adversarially Regularised Graph Auto-Encoder 46 | * `arga_vae`: Adversarially Regularised Variational Graph Auto-Encoder 47 | 48 | ## Cite 49 | 50 | Please cite following papers if you use this code in your own work: 51 | 52 | ``` 53 | @inproceedings{pan2018adversarially, 54 | title={Adversarially Regularized Graph Autoencoder for Graph Embedding.}, 55 | author={Pan, Shirui and Hu, Ruiqi and Long, Guodong and Jiang, Jing and Yao, Lina and Zhang, Chengqi}, 56 | booktitle={IJCAI}, 57 | pages={2609--2615}, 58 | year={2018} 59 | } 60 | ``` 61 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | tensorflow 2 | numpy 3 | scikit-learn 4 | scipy 5 | networkx 6 | munkres 7 | --------------------------------------------------------------------------------