"""
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
[docs]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
[docs] 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()]
[docs]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
[docs] 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()]
[docs] 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
[docs] 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)