def load_perterbued_data(dataset, ptb_rate, ptb_type="meta"):
if ptb_type == 'meta':
data = Dataset(root='/tmp/',
name=dataset.lower(),
setting='nettack',
seed=15,
require_mask=True)
data.x, data.y = data.features, data.labels
if ptb_rate > 0:
perturbed_data = PrePtbDataset(root='/tmp/',
name=dataset.lower(),
attack_method='meta',
ptb_rate=ptb_rate)
data.edge_index = perturbed_data.adj
else:
data.edge_index = data.adj
return data
elif ptb_type == 'random_add':
data = Dataset(root='/tmp/',
name=dataset.lower(),
setting='nettack',
seed=15,
require_mask=True)
data.x, data.y = data.features, data.labels
num_edge = data.adj.sum(axis=None) / 2
attacker = Random()
attacker.attack(data.adj,
n_perturbations=int(ptb_rate * num_edge),
type='add')
data.edge_index = attacker.modified_adj
return data
elif ptb_type == 'random_remove':
data = Dataset(root='/tmp/',
name=dataset.lower(),
setting='nettack',
seed=15,
require_mask=True)
data.x, data.y = data.features, data.labels
num_edge = data.adj.sum(axis=None) / 2
attacker = Random()
attacker.attack(data.adj,
n_perturbations=int(ptb_rate * num_edge),
type='remove')
data.edge_index = attacker.modified_adj
return data
raise Exception(f"the ptb_type of {ptb_type} has not been implemented")
np.random.seed(
15
) # Here the random seed is to split the train/val/test data, we need to set the random seed to be the same as that when you generate the perturbed graph
data = Dataset(root='/tmp/', name=args.dataset, setting='nettack')
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
if args.attack == 'no':
perturbed_adj = adj
if args.attack == 'random':
from deeprobust.graph.global_attack import Random
attacker = Random()
n_perturbations = int(args.ptb_rate * (adj.sum() // 2))
perturbed_adj = attacker.attack(adj, n_perturbations, type='add')
if args.attack == 'meta' or args.attack == 'nettack':
perturbed_data = PrePtbDataset(root='/tmp/',
name=args.dataset,
attack_method=args.attack,
ptb_rate=args.ptb_rate)
perturbed_adj = perturbed_data.adj
if args.attack == 'nettack':
idx_test = perturbed_data.target_nodes
np.random.seed(args.seed)
torch.manual_seed(args.seed)
model = GCN(nfeat=features.shape[1],
nhid=args.hidden,
adv_train_model = GCN(nfeat=features.shape[1],
nclass=labels.max() + 1,
nhid=16,
dropout=0,
with_relu=False,
with_bias=True,
device=device)
adv_train_model = adv_train_model.to(device)
adv_train_model.initialize()
n_perturbations = int(0.01 * (adj.sum() // 2))
for i in tqdm(range(100)):
# modified_adj = adversary.attack(features, adj)
modified_adj = adversary.attack(adj,
n_perturbations=n_perturbations,
type='add')
adv_train_model.fit(features,
modified_adj,
labels,
idx_train,
train_iters=50,
initialize=False)
adv_train_model.eval()
# test directly or fine tune
print('=== test on perturbed adj ===')
output = adv_train_model.predict()
acc_test = accuracy(output[idx_test], labels[idx_test])
print("Test set results:", "accuracy= {:.4f}".format(acc_test.item()))
print('=== testing GCN on perturbed graph ===')
model.fit(features, perturbed_adj, labels, idx_train)
output = model.output
acc_test = accuracy(output[idx_test], labels[idx_test])
print("Test set results:", "accuracy= {:.4f}".format(acc_test.item()))
# For poisoning attack, the adjacency matrix you have
# is alreay perturbed
print('=== Adversarial Training for Poisoning Attack===')
model.initialize()
n_perturbations = int(0.01 * (adj.sum() // 2))
for i in range(100):
# modified_adj = adversary.attack(features, adj)
adversary.attack(perturbed_adj,
n_perturbations=n_perturbations,
type='remove')
modified_adj = adversary.modified_adj
model.fit(features,
modified_adj,
labels,
idx_train,
train_iters=50,
initialize=False)
model.eval()
# test directly or fine tune
print('=== test on perturbed adj ===')
output = model.predict(features, perturbed_adj)
acc_test = accuracy(output[idx_test], labels[idx_test])
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
data = Dataset(root='/tmp/', name=args.dataset)
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
idx_unlabeled = np.union1d(idx_val, idx_test)
# Setup Attack Model
model = Random()
n_perturbations = int(args.ptb_rate * (adj.sum() // 2))
model.attack(adj, n_perturbations)
modified_adj = model.modified_adj
adj, features, labels = preprocess(adj,
features,
labels,
preprocess_adj=False,
sparse=True)
adj = adj.to(device)
features = features.to(device)
labels = labels.to(device)
modified_adj = normalize_adj(modified_adj)
modified_adj = sparse_mx_to_torch_sparse_tensor(modified_adj)
modified_adj = modified_adj.to(device)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
data = Dataset(root='/tmp/', name=args.dataset)
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
idx_unlabeled = np.union1d(idx_val, idx_test)
# Setup Attack Model
model = Random()
n_perturbations = int(args.ptb_rate * (adj.sum()//2))
modified_adj = model.attack(adj, n_perturbations)
adj, features, labels = preprocess(adj, features, labels, preprocess_adj=False, sparse=True)
adj = adj.to(device)
features = features.to(device)
labels = labels.to(device)
modified_adj = normalize_adj(modified_adj)
modified_adj = sparse_mx_to_torch_sparse_tensor(modified_adj)
modified_adj = modified_adj.to(device)
def test(adj):
''' test on GCN '''
# adj = normalize_adj_tensor(adj)
gcn = GCN(nfeat=features.shape[1],