File size: 19,244 Bytes
d38bce3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 |
"""
Code in this file is modified from https://github.com/abojchevski/node_embedding_attack
'Adversarial Attacks on Node Embeddings via Graph Poisoning'
Aleksandar Bojchevski and Stephan Günnemann, ICML 2019
http://proceedings.mlr.press/v97/bojchevski19a.html
Copyright (C) owned by the authors, 2019
"""
import numba
import numpy as np
import scipy.sparse as sp
from gensim.models import Word2Vec
import networkx as nx
from gensim.models import KeyedVectors
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import normalize
from sklearn.metrics import f1_score, roc_auc_score, average_precision_score, accuracy_score
class BaseEmbedding:
"""Base class for node embedding methods such as DeepWalk and Node2Vec.
"""
def __init__(self):
self.embedding = None
self.model = None
def evaluate_node_classification(self, labels, idx_train, idx_test,
normalize_embedding=True, lr_params=None):
"""Evaluate the node embeddings on the node classification task..
Parameters
---------
labels: np.ndarray, shape [n_nodes]
The ground truth labels
normalize_embedding: bool
Whether to normalize the embeddings
idx_train: np.array
Indices of training nodes
idx_test: np.array
Indices of test nodes
lr_params: dict
Parameters for the LogisticRegression model
Returns
-------
[numpy.array, float, float] :
Predictions from LR, micro F1 score and macro F1 score
"""
embedding_matrix = self.embedding
if normalize_embedding:
embedding_matrix = normalize(embedding_matrix)
features_train = embedding_matrix[idx_train]
features_test = embedding_matrix[idx_test]
labels_train = labels[idx_train]
labels_test = labels[idx_test]
if lr_params is None:
lr = LogisticRegression(solver='lbfgs', max_iter=1000, multi_class='auto')
else:
lr = LogisticRegression(**lr_params)
lr.fit(features_train, labels_train)
lr_z_predict = lr.predict(features_test)
f1_micro = f1_score(labels_test, lr_z_predict, average='micro')
f1_macro = f1_score(labels_test, lr_z_predict, average='macro')
test_acc = accuracy_score(labels_test, lr_z_predict)
print('Micro F1:', f1_micro)
print('Macro F1:', f1_macro)
return lr_z_predict, f1_micro, f1_macro
def evaluate_link_prediction(self, adj, node_pairs, normalize_embedding=True):
"""Evaluate the node embeddings on the link prediction task.
adj: sp.csr_matrix, shape [n_nodes, n_nodes]
Adjacency matrix of the graph
node_pairs: numpy.array, shape [n_pairs, 2]
Node pairs
normalize_embedding: bool
Whether to normalize the embeddings
Returns
-------
[numpy.array, float, float]
Inner product of embeddings, Area under ROC curve (AUC) score and average precision (AP) score
"""
embedding_matrix = self.embedding
if normalize_embedding:
embedding_matrix = normalize(embedding_matrix)
true = adj[node_pairs[:, 0], node_pairs[:, 1]].A1
scores = (embedding_matrix[node_pairs[:, 0]] * embedding_matrix[node_pairs[:, 1]]).sum(1)
# print(np.unique(true, return_counts=True))
try:
auc_score = roc_auc_score(true, scores)
except Exception as e:
auc_score = 0.00
print('ROC error')
ap_score = average_precision_score(true, scores)
print("AUC:", auc_score)
print("AP:", ap_score)
return scores, auc_score, ap_score
class Node2Vec(BaseEmbedding):
"""node2vec: Scalable Feature Learning for Networks. KDD'15.
To use this model, you need to "pip install node2vec" first.
Examples
----
>>> from deeprobust.graph.data import Dataset
>>> from deeprobust.graph.global_attack import NodeEmbeddingAttack
>>> from deeprobust.graph.defense import Node2Vec
>>> data = Dataset(root='/tmp/', name='cora_ml', seed=15)
>>> adj, features, labels = data.adj, data.features, data.labels
>>> idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
>>> # set up attack model
>>> attacker = NodeEmbeddingAttack()
>>> attacker.attack(adj, attack_type="remove", n_perturbations=1000)
>>> modified_adj = attacker.modified_adj
>>> print("Test Node2vec on clean graph")
>>> model = Node2Vec()
>>> model.fit(adj)
>>> model.evaluate_node_classification(labels, idx_train, idx_test)
>>> print("Test Node2vec on attacked graph")
>>> model = Node2Vec()
>>> model.fit(modified_adj)
>>> model.evaluate_node_classification(labels, idx_train, idx_test)
"""
def __init__(self):
# self.fit = self.node2vec_snap
super(Node2Vec, self).__init__()
self.fit = self.node2vec
def node2vec(self, adj, embedding_dim=64, walk_length=30, walks_per_node=10,
workers=8, window_size=10, num_neg_samples=1, p=4, q=1):
"""Compute Node2Vec embeddings for the given graph.
Parameters
----------
adj : sp.csr_matrix, shape [n_nodes, n_nodes]
Adjacency matrix of the graph
embedding_dim : int, optional
Dimension of the embedding
walks_per_node : int, optional
Number of walks sampled from each node
walk_length : int, optional
Length of each random walk
workers : int, optional
Number of threads (see gensim.models.Word2Vec process)
window_size : int, optional
Window size (see gensim.models.Word2Vec)
num_neg_samples : int, optional
Number of negative samples (see gensim.models.Word2Vec)
p : float
The hyperparameter p in node2vec
q : float
The hyperparameter q in node2vec
"""
walks = sample_n2v_random_walks(adj, walk_length, walks_per_node, p=p, q=q)
walks = [list(map(str, walk)) for walk in walks]
self.model = Word2Vec(walks, size=embedding_dim, window=window_size, min_count=0, sg=1, workers=workers,
iter=1, negative=num_neg_samples, hs=0, compute_loss=True)
self.embedding = self.model.wv.vectors[np.fromiter(map(int, self.model.wv.index2word), np.int32).argsort()]
class DeepWalk(BaseEmbedding):
"""DeepWalk: Online Learning of Social Representations. KDD'14. The implementation is
modified from https://github.com/abojchevski/node_embedding_attack
Examples
----
>>> from deeprobust.graph.data import Dataset
>>> from deeprobust.graph.global_attack import NodeEmbeddingAttack
>>> from deeprobust.graph.defense import DeepWalk
>>> data = Dataset(root='/tmp/', name='cora_ml', seed=15)
>>> adj, features, labels = data.adj, data.features, data.labels
>>> idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
>>> # set up attack model
>>> attacker = NodeEmbeddingAttack()
>>> attacker.attack(adj, attack_type="remove", n_perturbations=1000)
>>> modified_adj = attacker.modified_adj
>>> print("Test DeepWalk on clean graph")
>>> model = DeepWalk()
>>> model.fit(adj)
>>> model.evaluate_node_classification(labels, idx_train, idx_test)
>>> print("Test DeepWalk on attacked graph")
>>> model.fit(modified_adj)
>>> model.evaluate_node_classification(labels, idx_train, idx_test)
>>> print("Test DeepWalk SVD")
>>> model = DeepWalk(type="svd")
>>> model.fit(modified_adj)
>>> model.evaluate_node_classification(labels, idx_train, idx_test)
"""
def __init__(self, type="skipgram"):
super(DeepWalk, self).__init__()
if type == "skipgram":
self.fit = self.deepwalk_skipgram
elif type == "svd":
self.fit = self.deepwalk_svd
else:
raise NotImplementedError
def deepwalk_skipgram(self, adj, embedding_dim=64, walk_length=80, walks_per_node=10,
workers=8, window_size=10, num_neg_samples=1):
"""Compute DeepWalk embeddings for the given graph using the skip-gram formulation.
Parameters
----------
adj : sp.csr_matrix, shape [n_nodes, n_nodes]
Adjacency matrix of the graph
embedding_dim : int, optional
Dimension of the embedding
walks_per_node : int, optional
Number of walks sampled from each node
walk_length : int, optional
Length of each random walk
workers : int, optional
Number of threads (see gensim.models.Word2Vec process)
window_size : int, optional
Window size (see gensim.models.Word2Vec)
num_neg_samples : int, optional
Number of negative samples (see gensim.models.Word2Vec)
"""
walks = sample_random_walks(adj, walk_length, walks_per_node)
walks = [list(map(str, walk)) for walk in walks]
self.model = Word2Vec(walks, size=embedding_dim, window=window_size, min_count=0, sg=1, workers=workers,
iter=1, negative=num_neg_samples, hs=0, compute_loss=True)
self.embedding = self.model.wv.vectors[np.fromiter(map(int, self.model.wv.index2word), np.int32).argsort()]
def deepwalk_svd(self, adj, window_size=10, embedding_dim=64, num_neg_samples=1, sparse=True):
"""Compute DeepWalk embeddings for the given graph using the matrix factorization formulation.
adj: sp.csr_matrix, shape [n_nodes, n_nodes]
Adjacency matrix of the graph
window_size: int
Size of the window
embedding_dim: int
Size of the embedding
num_neg_samples: int
Number of negative samples
sparse: bool
Whether to perform sparse operations
Returns
------
np.ndarray, shape [num_nodes, embedding_dim]
Embedding matrix.
"""
sum_powers_transition = sum_of_powers_of_transition_matrix(adj, window_size)
deg = adj.sum(1).A1
deg[deg == 0] = 1
deg_matrix = sp.diags(1 / deg)
volume = adj.sum()
M = sum_powers_transition.dot(deg_matrix) * volume / (num_neg_samples * window_size)
log_M = M.copy()
log_M[M > 1] = np.log(log_M[M > 1])
log_M = log_M.multiply(M > 1)
if not sparse:
log_M = log_M.toarray()
Fu, Fv = self.svd_embedding(log_M, embedding_dim, sparse)
loss = np.linalg.norm(Fu.dot(Fv.T) - log_M, ord='fro')
self.embedding = Fu
return Fu, Fv, loss, log_M
def svd_embedding(self, x, embedding_dim, sparse=False):
"""Computes an embedding by selection the top (embedding_dim) largest singular-values/vectors.
:param x: sp.csr_matrix or np.ndarray
The matrix that we want to embed
:param embedding_dim: int
Dimension of the embedding
:param sparse: bool
Whether to perform sparse operations
:return: np.ndarray, shape [?, embedding_dim], np.ndarray, shape [?, embedding_dim]
Embedding matrices.
"""
if sparse:
U, s, V = sp.linalg.svds(x, embedding_dim)
else:
U, s, V = np.linalg.svd(x)
S = np.diag(s)
Fu = U.dot(np.sqrt(S))[:, :embedding_dim]
Fv = np.sqrt(S).dot(V)[:embedding_dim, :].T
return Fu, Fv
def sample_random_walks(adj, walk_length, walks_per_node, seed=None):
"""Sample random walks of fixed length from each node in the graph in parallel.
Parameters
----------
adj : sp.csr_matrix, shape [n_nodes, n_nodes]
Sparse adjacency matrix
walk_length : int
Random walk length
walks_per_node : int
Number of random walks per node
seed : int or None
Random seed
Returns
-------
walks : np.ndarray, shape [num_walks * num_nodes, walk_length]
The sampled random walks
"""
if seed is None:
seed = np.random.randint(0, 100000)
adj = sp.csr_matrix(adj)
random_walks = _random_walk(adj.indptr,
adj.indices,
walk_length,
walks_per_node,
seed).reshape([-1, walk_length])
return random_walks
@numba.jit(nopython=True, parallel=True)
def _random_walk(indptr, indices, walk_length, walks_per_node, seed):
"""Sample r random walks of length l per node in parallel from the graph.
Parameters
----------
indptr : array-like
Pointer for the edges of each node
indices : array-like
Edges for each node
walk_length : int
Random walk length
walks_per_node : int
Number of random walks per node
seed : int
Random seed
Returns
-------
walks : array-like, shape [r*N*l]
The sampled random walks
"""
np.random.seed(seed)
N = len(indptr) - 1
walks = []
for ir in range(walks_per_node):
for n in range(N):
for il in range(walk_length):
walks.append(n)
n = np.random.choice(indices[indptr[n]:indptr[n + 1]])
return np.array(walks)
def sample_n2v_random_walks(adj, walk_length, walks_per_node, p, q, seed=None):
"""Sample node2vec random walks of fixed length from each node in the graph in parallel.
Parameters
----------
adj : sp.csr_matrix, shape [n_nodes, n_nodes]
Sparse adjacency matrix
walk_length : int
Random walk length
walks_per_node : int
Number of random walks per node
p: float
The probability to go back
q: float,
The probability to go explore undiscovered parts of the graphs
seed : int or None
Random seed
Returns
-------
walks : np.ndarray, shape [num_walks * num_nodes, walk_length]
The sampled random walks
"""
if seed is None:
seed = np.random.randint(0, 100000)
adj = sp.csr_matrix(adj)
random_walks = _n2v_random_walk(adj.indptr,
adj.indices,
walk_length,
walks_per_node,
p,
q,
seed)
return random_walks
@numba.jit(nopython=True)
def random_choice(arr, p):
"""Similar to `numpy.random.choice` and it suppors p=option in numba.
refer to <https://github.com/numba/numba/issues/2539#issuecomment-507306369>
Parameters
----------
arr : 1-D array-like
p : 1-D array-like
The probabilities associated with each entry in arr
Returns
-------
samples : ndarray
The generated random samples
"""
return arr[np.searchsorted(np.cumsum(p), np.random.random(), side="right")]
@numba.jit(nopython=True)
def _n2v_random_walk(indptr,
indices,
walk_length,
walks_per_node,
p,
q,
seed):
"""Sample r random walks of length l per node in parallel from the graph.
Parameters
----------
indptr : array-like
Pointer for the edges of each node
indices : array-like
Edges for each node
walk_length : int
Random walk length
walks_per_node : int
Number of random walks per node
p: float
The probability to go back
q: float,
The probability to go explore undiscovered parts of the graphs
seed : int
Random seed
Returns
-------
walks : list generator, shape [r, N*l]
The sampled random walks
"""
np.random.seed(seed)
N = len(indptr) - 1
for _ in range(walks_per_node):
for n in range(N):
walk = [n]
current_node = n
previous_node = N
previous_node_neighbors = np.empty(0, dtype=np.int32)
for _ in range(walk_length - 1):
neighbors = indices[indptr[current_node]:indptr[current_node + 1]]
if neighbors.size == 0:
break
probability = np.array([1 / q] * neighbors.size)
probability[previous_node == neighbors] = 1 / p
for i, nbr in enumerate(neighbors):
if np.any(nbr == previous_node_neighbors):
probability[i] = 1.
norm_probability = probability / np.sum(probability)
current_node = random_choice(neighbors, norm_probability)
walk.append(current_node)
previous_node_neighbors = neighbors
previous_node = current_node
yield walk
def sum_of_powers_of_transition_matrix(adj, pow):
"""Computes \sum_{r=1}^{pow) (D^{-1}A)^r.
Parameters
-----
adj: sp.csr_matrix, shape [n_nodes, n_nodes]
Adjacency matrix of the graph
pow: int
Power exponent
Returns
----
sp.csr_matrix
Sum of powers of the transition matrix of a graph.
"""
deg = adj.sum(1).A1
deg[deg == 0] = 1
transition_matrix = sp.diags(1 / deg).dot(adj)
sum_of_powers = transition_matrix
last = transition_matrix
for i in range(1, pow):
last = last.dot(transition_matrix)
sum_of_powers += last
return sum_of_powers
if __name__ == "__main__":
from deeprobust.graph.data import Dataset
from deeprobust.graph.global_attack import NodeEmbeddingAttack
dataset_str = 'cora_ml'
data = Dataset(root='/tmp/', name=dataset_str, seed=15)
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
model = NodeEmbeddingAttack()
model.attack(adj, attack_type="add_by_remove", n_perturbations=1000, n_candidates=10000)
modified_adj = model.modified_adj
# train defense model
print("Test DeepWalk on clean graph")
model = DeepWalk()
model.fit(adj)
model.evaluate_node_classification(labels, idx_train, idx_test)
# model.evaluate_node_classification(labels, idx_train, idx_test, lr_params={"max_iter": 10})
print("Test DeepWalk on attacked graph")
model.fit(modified_adj)
model.evaluate_node_classification(labels, idx_train, idx_test)
print("\t link prediciton...")
model.evaluate_link_prediction(modified_adj, np.array(adj.nonzero()).T)
print("Test DeepWalk SVD")
model = DeepWalk(type="svd")
model.fit(modified_adj)
model.evaluate_node_classification(labels, idx_train, idx_test)
# train defense model
print("Test Node2vec on clean graph")
model = Node2Vec()
model.fit(adj)
model.evaluate_node_classification(labels, idx_train, idx_test)
print("Test Node2vec on attacked graph")
model = Node2Vec()
model.fit(modified_adj)
model.evaluate_node_classification(labels, idx_train, idx_test)
|