def attack(self, ori_features, ori_adj, labels, idx_train, idx_unlabeled, perturbations, ll_constraint=True, ll_cutoff=0.004):
ori_adj, ori_features, labels = utils.to_tensor(ori_adj, ori_features, labels, device=self.device)
labels_self_training = self.self_training_label(labels, idx_train)
self.sparse_features = sp.issparse(ori_features)
modified_adj = ori_adj
modified_features = ori_features
for i in tqdm(range(perturbations), desc="Perturbing graph"):
self._initialize()
if self.attack_structure:
modified_adj = self.get_modified_adj(ori_adj)
self.adj_grad_sum.data.fill_(0)
if self.attack_features:
modified_features = ori_features + self.feature_changes
self.feature_grad_sum.data.fill_(0)
self.inner_train(modified_features, modified_adj, idx_train, idx_unlabeled, labels, labels_self_training)
adj_meta_score = torch.tensor(0.0).to(self.device)
feature_meta_score = torch.tensor(0.0).to(self.device)
if self.attack_structure:
adj_meta_score = self.get_adj_score(self.adj_grad_sum, modified_adj, ori_adj, ll_constraint, ll_cutoff)
if self.attack_features:
feature_meta_score = self.get_feature_score(self.feature_grad_sum, modified_features)
if adj_meta_score.max() >= feature_meta_score.max():
adj_meta_argmax = torch.argmax(adj_meta_score)
row_idx, col_idx = utils.unravel_index(adj_meta_argmax, ori_adj.shape)
self.adj_changes.data[row_idx][col_idx] += (-2 * modified_adj[row_idx][col_idx] + 1)
self.adj_changes.data[col_idx][row_idx] += (-2 * modified_adj[row_idx][col_idx] + 1)
else:
feature_meta_argmax = torch.argmax(feature_meta_score)
row_idx, col_idx = utils.unravel_index(feature_meta_argmax, ori_features.shape)
self.features_changes.data[row_idx][col_idx] += (-2 * modified_features[row_idx][col_idx] + 1)
if self.attack_structure:
self.modified_adj = self.get_modified_adj(ori_adj).detach()
if self.attack_features:
self.modified_features = self.get_modified_features(ori_features).detach()
def attack(self,
ori_features,
ori_adj,
labels,
idx_train,
idx_unlabeled,
n_perturbations,
ll_constraint=True,
ll_cutoff=0.004):
"""Generate n_perturbations on the input graph.
Parameters
----------
ori_features :
Original (unperturbed) node feature matrix
ori_adj :
Original (unperturbed) adjacency matrix
labels :
node labels
idx_train :
node training indices
idx_unlabeled:
unlabeled nodes indices
n_perturbations : int
Number of perturbations on the input graph. Perturbations could
be edge removals/additions or feature removals/additions.
ll_constraint: bool
whether to exert the likelihood ratio test constraint
ll_cutoff : float
The critical value for the likelihood ratio test of the power law distributions.
See the Chi square distribution with one degree of freedom. Default value 0.004
corresponds to a p-value of roughly 0.95. It would be ignored if `ll_constraint`
is False.
"""
ori_adj, ori_features, labels = utils.to_tensor(ori_adj,
ori_features,
labels,
device=self.device)
labels_self_training = self.self_training_label(labels, idx_train)
self.sparse_features = sp.issparse(ori_features)
modified_adj = ori_adj
modified_features = ori_features
for i in tqdm(range(n_perturbations), desc="Perturbing graph"):
self._initialize()
if self.attack_structure:
modified_adj = self.get_modified_adj(ori_adj)
self.adj_grad_sum.data.fill_(0)
if self.attack_features:
modified_features = ori_features + self.feature_changes
self.feature_grad_sum.data.fill_(0)
self.inner_train(modified_features, modified_adj, idx_train,
idx_unlabeled, labels, labels_self_training)
adj_meta_score = torch.tensor(0.0).to(self.device)
feature_meta_score = torch.tensor(0.0).to(self.device)
if self.attack_structure:
adj_meta_score = self.get_adj_score(self.adj_grad_sum,
modified_adj, ori_adj,
ll_constraint, ll_cutoff)
if self.attack_features:
feature_meta_score = self.get_feature_score(
self.feature_grad_sum, modified_features)
if adj_meta_score.max() >= feature_meta_score.max():
adj_meta_argmax = torch.argmax(adj_meta_score)
row_idx, col_idx = utils.unravel_index(adj_meta_argmax,
ori_adj.shape)
self.adj_changes.data[row_idx][col_idx] += (
-2 * modified_adj[row_idx][col_idx] + 1)
self.adj_changes.data[col_idx][row_idx] += (
-2 * modified_adj[row_idx][col_idx] + 1)
else:
feature_meta_argmax = torch.argmax(feature_meta_score)
row_idx, col_idx = utils.unravel_index(feature_meta_argmax,
ori_features.shape)
self.features_changes.data[row_idx][col_idx] += (
-2 * modified_features[row_idx][col_idx] + 1)
if self.attack_structure:
self.modified_adj = self.get_modified_adj(ori_adj).detach()
if self.attack_features:
self.modified_features = self.get_modified_features(
ori_features).detach()
