import random
import numpy as np
import scipy.sparse as sp
from deeprobust.graph.global_attack import BaseAttack
[docs]class DICE(BaseAttack):
"""As is described in ADVERSARIAL ATTACKS ON GRAPH NEURAL NETWORKS VIA META LEARNING (ICLR'19),
'DICE (delete internally, connect externally) is a baseline where, for each perturbation,
we randomly choose whether to insert or remove an edge. Edges are only removed between
nodes from the same classes, and only inserted between nodes from different classes.
Parameters
----------
model :
model to attack. Default `None`.
nnodes : int
number of nodes in the input graph
attack_structure : bool
whether to attack graph structure
attack_features : bool
whether to attack node features
device: str
'cpu' or 'cuda'
Examples
--------
>>> from deeprobust.graph.data import Dataset
>>> from deeprobust.graph.global_attack import DICE
>>> data = Dataset(root='/tmp/', name='cora')
>>> adj, features, labels = data.adj, data.features, data.labels
>>> model = DICE()
>>> model.attack(adj, labels, n_perturbations=10)
>>> modified_adj = model.modified_adj
"""
def __init__(self, model=None, nnodes=None, attack_structure=True, attack_features=False, device='cpu'):
super(DICE, self).__init__(model, nnodes, attack_structure=attack_structure, attack_features=attack_features, device=device)
assert not self.attack_features, 'DICE does NOT support attacking features'
[docs] def attack(self, ori_adj, labels, n_perturbations, **kwargs):
"""Delete internally, connect externally. This baseline has all true class labels
(train and test) available.
Parameters
----------
ori_adj : scipy.sparse.csr_matrix
Original (unperturbed) adjacency matrix.
labels:
node labels
n_perturbations : int
Number of edge removals/additions.
Returns
-------
None.
"""
# ori_adj: sp.csr_matrix
print('number of pertubations: %s' % n_perturbations)
modified_adj = ori_adj.tolil()
remove_or_insert = np.random.choice(2, n_perturbations)
n_remove = sum(remove_or_insert)
nonzero = set(zip(*ori_adj.nonzero()))
indices = sp.triu(modified_adj).nonzero()
possible_indices = [x for x in zip(indices[0], indices[1])
if labels[x[0]] == labels[x[1]]]
remove_indices = np.random.permutation(possible_indices)[: n_remove]
modified_adj[remove_indices[:, 0], remove_indices[:, 1]] = 0
modified_adj[remove_indices[:, 1], remove_indices[:, 0]] = 0
n_insert = n_perturbations - n_remove
# # sample edges to add
# nonzero = nonzero
# edges = self.random_sample_edges(adj, n_insert, exclude=nonzero)
# for n1, n2 in edges:
# modified_adj[n1, n2] += 1
# modified_adj[n2, n1] += 1
# sample edges to add
for i in range(n_insert):
# select a node
node1 = np.random.randint(ori_adj.shape[0])
possible_nodes = [x for x in range(ori_adj.shape[0])
if labels[x] != labels[node1] and modified_adj[x, node1] == 0]
# select another node
node2 = possible_nodes[np.random.randint(len(possible_nodes))]
modified_adj[node1, node2] = 1
modified_adj[node2, node1] = 1
self.check_adj(modified_adj)
self.modified_adj = modified_adj
[docs] def sample_forever(self, adj, exclude):
"""Randomly random sample edges from adjacency matrix, `exclude` is a set
which contains the edges we do not want to sample and the ones already sampled
"""
while True:
# t = tuple(np.random.randint(0, adj.shape[0], 2))
t = tuple(random.sample(range(0, adj.shape[0]), 2))
if t not in exclude:
yield t
exclude.add(t)
exclude.add((t[1], t[0]))
def random_sample_edges(self, adj, n, exclude):
itr = self.sample_forever(adj, exclude=exclude)
return [next(itr) for _ in range(n)]