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"""
Adversarial Attacks on Neural Networks for Graph Data. ICML 2018.
https://arxiv.org/abs/1806.02371
Author's Implementation
https://github.com/Hanjun-Dai/graph_adversarial_attack
This part of code is adopted from the author's implementation (Copyright (c) 2018 Dai, Hanjun and Li, Hui and Tian, Tian and Huang, Xin and Wang, Lin and Zhu, Jun and Song, Le) but modified
to be integrated into the repository.
"""
import os
import sys
import numpy as np
import torch
import networkx as nx
import random
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tqdm import tqdm
from copy import deepcopy
import pickle as cp
from deeprobust.graph.utils import *
import scipy.sparse as sp
from scipy.sparse.linalg import eigsh
from deeprobust.graph import utils
class StaticGraph(object):
graph = None
@staticmethod
def get_gsize():
return torch.Size( (len(StaticGraph.graph), len(StaticGraph.graph)) )
class GraphNormTool(object):
def __init__(self, normalize, gm, device):
self.adj_norm = normalize
self.gm = gm
g = StaticGraph.graph
edges = np.array(g.edges(), dtype=np.int64)
rev_edges = np.array([edges[:, 1], edges[:, 0]], dtype=np.int64)
# self_edges = np.array([range(len(g)), range(len(g))], dtype=np.int64)
# edges = np.hstack((edges.T, rev_edges, self_edges))
edges = np.hstack((edges.T, rev_edges))
idxes = torch.LongTensor(edges)
values = torch.ones(idxes.size()[1])
self.raw_adj = torch.sparse.FloatTensor(idxes, values, StaticGraph.get_gsize())
self.raw_adj = self.raw_adj.to(device)
self.normed_adj = self.raw_adj.clone()
if self.adj_norm:
if self.gm == 'gcn':
self.normed_adj = utils.normalize_adj_tensor(self.normed_adj, sparse=True)
# GraphLaplacianNorm(self.normed_adj)
else:
self.normed_adj = utils.degree_normalize_adj_tensor(self.normed_adj, sparse=True)
# GraphDegreeNorm(self.normed_adj)
def norm_extra(self, added_adj = None):
if added_adj is None:
return self.normed_adj
new_adj = self.raw_adj + added_adj
if self.adj_norm:
if self.gm == 'gcn':
new_adj = utils.normalize_adj_tensor(new_adj, sparse=True)
else:
new_adj = utils.degree_normalize_adj_tensor(new_adj, sparse=True)
return new_adj
class ModifiedGraph(object):
def __init__(self, directed_edges = None, weights = None):
self.edge_set = set() #(first, second)
self.node_set = set(range(StaticGraph.get_gsize()[0]))
self.node_set = np.arange(StaticGraph.get_gsize()[0])
if directed_edges is not None:
self.directed_edges = deepcopy(directed_edges)
self.weights = deepcopy(weights)
else:
self.directed_edges = []
self.weights = []
def add_edge(self, x, y, z):
assert x is not None and y is not None
if x == y:
return
for e in self.directed_edges:
if e[0] == x and e[1] == y:
return
if e[1] == x and e[0] == y:
return
self.edge_set.add((x, y)) # (first, second)
self.edge_set.add((y, x)) # (second, first)
self.directed_edges.append((x, y))
# assert z < 0
self.weights.append(z)
def get_extra_adj(self, device):
if len(self.directed_edges):
edges = np.array(self.directed_edges, dtype=np.int64)
rev_edges = np.array([edges[:, 1], edges[:, 0]], dtype=np.int64)
edges = np.hstack((edges.T, rev_edges))
idxes = torch.LongTensor(edges)
values = torch.Tensor(self.weights + self.weights)
added_adj = torch.sparse.FloatTensor(idxes, values, StaticGraph.get_gsize())
added_adj = added_adj.to(device)
return added_adj
else:
return None
def get_possible_nodes(self, target_node):
# connected = set()
connected = [target_node]
for n1, n2 in self.edge_set:
if n1 == target_node:
# connected.add(target_node)
connected.append(n2)
return np.setdiff1d(self.node_set, np.array(connected))
# return self.node_set - connected
class NodeAttackEnv(object):
"""Node attack environment. It executes an action and then change the
environment status (modify the graph).
"""
def __init__(self, features, labels, all_targets, list_action_space, classifier, num_mod=1, reward_type='binary'):
self.classifier = classifier
self.list_action_space = list_action_space
self.features = features
self.labels = labels
self.all_targets = all_targets
self.num_mod = num_mod
self.reward_type = reward_type
def setup(self, target_nodes):
self.target_nodes = target_nodes
self.n_steps = 0
self.first_nodes = None
self.rewards = None
self.binary_rewards = None
self.modified_list = []
for i in range(len(self.target_nodes)):
self.modified_list.append(ModifiedGraph())
self.list_acc_of_all = []
def step(self, actions):
"""run actions and get rewards
"""
if self.first_nodes is None: # pick the first node of edge
assert self.n_steps % 2 == 0
self.first_nodes = actions[:]
else:
for i in range(len(self.target_nodes)):
# assert self.first_nodes[i] != actions[i]
# deleta an edge from the graph
self.modified_list[i].add_edge(self.first_nodes[i], actions[i], -1.0)
self.first_nodes = None
self.banned_list = None
self.n_steps += 1
if self.isTerminal():
# only calc reward when its terminal
acc_list = []
loss_list = []
# for i in tqdm(range(len(self.target_nodes))):
for i in (range(len(self.target_nodes))):
device = self.labels.device
extra_adj = self.modified_list[i].get_extra_adj(device=device)
adj = self.classifier.norm_tool.norm_extra(extra_adj)
output = self.classifier(self.features, adj)
loss, acc = loss_acc(output, self.labels, self.all_targets, avg_loss=False)
# _, loss, acc = self.classifier(self.features, Variable(adj), self.all_targets, self.labels, avg_loss=False)
cur_idx = self.all_targets.index(self.target_nodes[i])
acc = np.copy(acc.double().cpu().view(-1).numpy())
loss = loss.data.cpu().view(-1).numpy()
self.list_acc_of_all.append(acc)
acc_list.append(acc[cur_idx])
loss_list.append(loss[cur_idx])
self.binary_rewards = (np.array(acc_list) * -2.0 + 1.0).astype(np.float32)
if self.reward_type == 'binary':
self.rewards = (np.array(acc_list) * -2.0 + 1.0).astype(np.float32)
else:
assert self.reward_type == 'nll'
self.rewards = np.array(loss_list).astype(np.float32)
def sample_pos_rewards(self, num_samples):
assert self.list_acc_of_all is not None
cands = []
for i in range(len(self.list_acc_of_all)):
succ = np.where( self.list_acc_of_all[i] < 0.9 )[0]
for j in range(len(succ)):
cands.append((i, self.all_targets[succ[j]]))
if num_samples > len(cands):
return cands
random.shuffle(cands)
return cands[0:num_samples]
def uniformRandActions(self):
# TODO: here only support deleting edges
# seems they sample first node from 2-hop neighbours
act_list = []
offset = 0
for i in range(len(self.target_nodes)):
cur_node = self.target_nodes[i]
region = self.list_action_space[cur_node]
if self.first_nodes is not None and self.first_nodes[i] is not None:
region = self.list_action_space[self.first_nodes[i]]
if region is None: # singleton node
cur_action = np.random.randint(len(self.list_action_space))
else: # select from neighbours or 2-hop neighbours
cur_action = region[np.random.randint(len(region))]
act_list.append(cur_action)
return act_list
def isTerminal(self):
if self.n_steps == 2 * self.num_mod:
return True
return False
def getStateRef(self):
cp_first = [None] * len(self.target_nodes)
if self.first_nodes is not None:
cp_first = self.first_nodes
return zip(self.target_nodes, self.modified_list, cp_first)
def cloneState(self):
cp_first = [None] * len(self.target_nodes)
if self.first_nodes is not None:
cp_first = self.first_nodes[:]
return list(zip(self.target_nodes[:], deepcopy(self.modified_list), cp_first))
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