acc_test = accuracy(output[idx_test], labels[idx_test])
print("Test set results:", "accuracy= {:.4f}".format(acc_test.item()))
print('=== Adversarial Training for Evasion Attack===')
adversary = Random()
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 original adj ===')
model.fit(features, 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()))
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()
