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")
perturbed_adj, features, labels = preprocess(perturbed_adj,
features,
labels,
preprocess_adj=False,
device=device)
labels = labels.cuda()
perturbed_adj = to_sparse(perturbed_adj.cuda())
features = features.cuda()
perturbed_adj = ut.normalize_adj_tensor(perturbed_adj, True)
features[features > 1] = 1
data = load_data(args.data,
normalize_feature=args.no_fea_norm,
missing_rate=args.missing_rate,
cuda=True)
data.x = features
data.y = labels
data.adj = perturbed_adj
data.train_mask, data.val_mask, data.test_mask = idx_train, idx_val, idx_test
nfeat = data.x.size(1)
nclass = int(data.y.max()) + 1
net = getattr(models, args.model)(nfeat,
args.hid,
nclass,
dropout=args.dropout,
nhead=args.nhead,
nlayer=args.nlayer,
norm_mode=args.norm_mode,
norm_scale=args.norm_scale,
residual=args.residual)