def fit(self,
features,
adj,
labels,
idx_train,
idx_val=None,
threshold=0.01,
train_iters=200,
initialize=True,
verbose=True):
self.threshold = threshold
modified_adj = self.drop_dissimilar_edges(features, adj)
# modified_adj_tensor = utils.sparse_mx_to_torch_sparse_tensor(self.modified_adj)
features, modified_adj, labels = utils.to_tensor(features,
modified_adj,
labels,
device=self.device)
self.modified_adj = modified_adj
self.features = features
self.labels = labels
super().fit(features,
modified_adj,
labels,
idx_train,
idx_val,
train_iters=train_iters,
initialize=initialize,
verbose=verbose)
def attack(self, ori_features, ori_adj, labels, idx_train, perturbations):
victim_model = self.surrogate
self.sparse_features = sp.issparse(ori_features)
ori_adj, ori_features, labels = utils.to_tensor(ori_adj,
ori_features,
labels,
device=self.device)
modified_adj = ori_adj
victim_model.eval()
s_e = self.calc_importance_edge(ori_features,
ori_adj,
labels,
idx_train,
steps=10)
import ipdb
ipdb.set_trace()
for t in tqdm(range(perturbations)):
modified_adj
self.adj_changes.data.copy_(torch.tensor(best_s))
self.modified_adj = self.get_modified_adj(ori_adj).detach()
def attack(self, ori_features, ori_adj, labels, idx_train, perturbations):
victim_model = self.surrogate
self.sparse_features = sp.issparse(ori_features)
ori_adj, ori_features, labels = utils.to_tensor(ori_adj,
ori_features,
labels,
device=self.device)
victim_model.eval()
epochs = 200
for t in tqdm(range(epochs)):
modified_adj = self.get_modified_adj(ori_adj)
adj_norm = utils.normalize_adj_tensor(modified_adj)
output = victim_model(ori_features, adj_norm)
# loss = F.nll_loss(output[idx_train], labels[idx_train])
loss = self._loss(output[idx_train], labels[idx_train])
adj_grad = torch.autograd.grad(loss, self.adj_changes)[0]
if self.loss_type == 'CE':
lr = 200 / np.sqrt(t + 1)
self.adj_changes.data.add_(lr * adj_grad)
if self.loss_type == 'CW':
lr = 0.1 / np.sqrt(t + 1)
self.adj_changes.data.add_(lr * adj_grad)
self.projection(perturbations)
self.random_sample(ori_adj, ori_features, labels, idx_train,
perturbations)
self.modified_adj = self.get_modified_adj(ori_adj).detach()
def fit(self,
features,
adj,
labels,
idx_train,
idx_val=None,
k=50,
train_iters=200,
initialize=True,
verbose=True):
modified_adj = self.truncatedSVD(adj, k=k)
# modified_adj_tensor = utils.sparse_mx_to_torch_sparse_tensor(self.modified_adj)
features, modified_adj, labels = utils.to_tensor(features,
modified_adj,
labels,
device=self.device)
self.modified_adj = modified_adj
self.features = features
self.labels = labels
super().fit(features,
modified_adj,
labels,
idx_train,
idx_val,
train_iters=train_iters,
initialize=initialize,
verbose=verbose)
def fit(self, features, adj, labels, idx_train, idx_val=None, train_iters=200, initialize=True, verbose=False, normalize=True):
'''
train the gcn model, when idx_val is not None, pick the best model
according to the validation loss
'''
self.device = self.gc1.weight.device
if initialize:
self.initialize()
if type(adj) is not torch.Tensor:
features, adj, labels = utils.to_tensor(features, adj, labels, device=self.device)
else:
features = features.to(self.device)
adj = adj.to(self.device)
labels = labels.to(self.device)
if normalize:
if utils.is_sparse_tensor(adj):
adj_norm = utils.normalize_adj_tensor(adj, sparse=True)
else:
adj_norm = utils.normalize_adj_tensor(adj)
else:
adj_norm = adj
self.adj_norm = adj_norm
self.features = features
self.labels = labels
if idx_val is None:
self._train_without_val(labels, idx_train, train_iters, verbose)
else:
self._train_with_val(labels, idx_train, idx_val, train_iters, verbose)
def predict(self, features=None, adj=None):
"""By default, the inputs should be unnormalized data
Parameters
----------
features :
node features. If `features` and `adj` are not given, this function will use previous stored `features` and `adj` from training to make predictions.
adj :
adjcency matrix. If `features` and `adj` are not given, this function will use previous stored `features` and `adj` from training to make predictions.
Returns
-------
torch.FloatTensor
output (log probabilities) of GCN
"""
self.eval()
if features is None and adj is None:
return self.forward(self.features, self.adj_norm)
else:
if type(adj) is not torch.Tensor:
features, adj = utils.to_tensor(features,
adj,
device=self.device)
self.features = features
if utils.is_sparse_tensor(adj):
self.adj_norm = utils.normalize_adj_tensor(adj, sparse=True)
else:
self.adj_norm = utils.normalize_adj_tensor(adj)
return self.forward(self.features, self.adj_norm)
def fit(self, features, adj, labels, idx_train, idx_val=None, train_iters=200, initialize=True, verbose=False, normalize=True, patience=500, **kwargs):
if initialize:
self.initialize()
if type(adj) is not torch.Tensor:
features, adj, labels = utils.to_tensor(features, adj, labels, device=self.device)
else:
features = features.to(self.device)
adj = adj.to(self.device)
labels = labels.to(self.device)
if normalize:
if utils.is_sparse_tensor(adj):
adj_norm = utils.normalize_adj_tensor(adj, sparse=True)
else:
adj_norm = utils.normalize_adj_tensor(adj)
else:
adj_norm = adj
self.adj_norm = adj_norm
self.features = features
self.labels = labels
if idx_val is None:
self._train_without_val(labels, idx_train, train_iters, verbose)
else:
if patience < train_iters:
self._train_with_early_stopping(labels, idx_train, idx_val, train_iters, patience, verbose)
else:
self._train_with_val(labels, idx_train, idx_val, train_iters, verbose)
def fit(self,
features,
adj,
labels,
idx_train,
idx_val=None,
idx_test=None,
train_iters=200,
verbose=True,
attention=None):
adj, features, labels = utils.to_tensor(adj.todense(),
features.todense(),
labels,
device=self.device)
self.features, self.labels = features, labels
self.adj_norm1 = self._normalize_adj(adj, power=-1 / 2)
self.adj_norm2 = self._normalize_adj(adj, power=-1)
print('=== training rgcn model ===')
self._initialize()
if idx_val is None:
self._train_without_val(labels, idx_train, train_iters, verbose)
else:
self._train_with_val(labels, idx_train, idx_val, train_iters,
verbose)
def attack(self,
ori_features,
ori_adj,
labels,
idx_train,
n_perturbations,
epochs=200,
**kwargs):
"""Generate 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
n_perturbations : int
Number of perturbations on the input graph. Perturbations could
be edge removals/additions or feature removals/additions.
epochs:
number of training epochs
"""
victim_model = self.surrogate
self.sparse_features = sp.issparse(ori_features)
ori_adj, ori_features, labels = utils.to_tensor(ori_adj,
ori_features,
labels,
device=self.device)
victim_model.eval()
for t in tqdm(range(epochs)):
modified_adj = self.get_modified_adj(ori_adj)
adj_norm = utils.normalize_adj_tensor(modified_adj)
output = victim_model(ori_features, adj_norm)
# loss = F.nll_loss(output[idx_train], labels[idx_train])
loss = self._loss(output[idx_train], labels[idx_train])
adj_grad = torch.autograd.grad(loss, self.adj_changes)[0]
if self.loss_type == 'CE':
lr = 200 / np.sqrt(t + 1)
self.adj_changes.data.add_(lr * adj_grad)
if self.loss_type == 'CW':
lr = 0.1 / np.sqrt(t + 1)
self.adj_changes.data.add_(lr * adj_grad)
self.projection(n_perturbations)
self.random_sample(ori_adj, ori_features, labels, idx_train,
n_perturbations)
self.modified_adj = self.get_modified_adj(ori_adj).detach()
self.check_adj_tensor(self.modified_adj)
def fit(self,
features,
adj,
labels,
idx_train,
idx_val=None,
k=50,
train_iters=200,
initialize=True,
verbose=True,
**kwargs):
"""First perform rank-k approximation of adjacency matrix via
truncated SVD, and then train the gcn model on the processed graph,
when idx_val is not None, pick the best model according to
the validation loss.
Parameters
----------
features :
node features
adj :
the adjacency matrix. The format could be torch.tensor or scipy matrix
labels :
node labels
idx_train :
node training indices
idx_val :
node validation indices. If not given (None), GCN training process will not adpot early stopping
k : int
number of singular values and vectors to compute.
train_iters : int
number of training epochs
initialize : bool
whether to initialize parameters before training
verbose : bool
whether to show verbose logs
"""
modified_adj = self.truncatedSVD(adj, k=k)
self.k = k
# modified_adj_tensor = utils.sparse_mx_to_torch_sparse_tensor(self.modified_adj)
features, modified_adj, labels = utils.to_tensor(features,
modified_adj,
labels,
device=self.device)
self.modified_adj = modified_adj
self.features = features
self.labels = labels
super().fit(features,
modified_adj,
labels,
idx_train,
idx_val,
train_iters=train_iters,
initialize=initialize,
verbose=verbose)
def fit(self,
features,
adj,
labels,
idx_train,
idx_val=None,
threshold=0.01,
train_iters=200,
initialize=True,
verbose=True,
**kwargs):
"""First drop dissimilar edges with similarity smaller than given
threshold and then train the gcn model on the processed graph.
When idx_val is not None, pick the best model according to the
validation loss.
Parameters
----------
features :
node features
adj :
the adjacency matrix. The format could be torch.tensor or scipy matrix
labels :
node labels
idx_train :
node training indices
idx_val :
node validation indices. If not given (None), GCN training process will not adpot early stopping
threshold : float
similarity threshold for dropping edges. If two connected nodes with similarity smaller than threshold, the edge between them will be removed.
train_iters : int
number of training epochs
initialize : bool
whether to initialize parameters before training
verbose : bool
whether to show verbose logs
"""
self.threshold = threshold
modified_adj = self.drop_dissimilar_edges(features, adj)
# modified_adj_tensor = utils.sparse_mx_to_torch_sparse_tensor(self.modified_adj)
features, modified_adj, labels = utils.to_tensor(features,
modified_adj,
labels,
device=self.device)
self.modified_adj = modified_adj
self.features = features
self.labels = labels
super().fit(features,
modified_adj,
labels,
idx_train,
idx_val,
train_iters=train_iters,
initialize=initialize,
verbose=verbose)
def attack(self, ori_features, ori_adj, labels, idx_train, target_node, n_perturbations, verbose=False, **kwargs):
"""Generate perturbations on the input graph.
Parameters
----------
ori_features : scipy.sparse.csr_matrix
Original (unperturbed) adjacency matrix
ori_adj : scipy.sparse.csr_matrix
Original (unperturbed) node feature matrix
labels :
node labels
idx_train:
training node indices
target_node : int
target node index to be attacked
n_perturbations : int
Number of perturbations on the input graph. Perturbations could
be edge removals/additions or feature removals/additions.
"""
modified_adj = ori_adj.todense()
modified_features = ori_features.todense()
modified_adj, modified_features, labels = utils.to_tensor(modified_adj, modified_features, labels, device=self.device)
self.surrogate.eval()
if verbose == True:
print('number of pertubations: %s' % n_perturbations)
pseudo_labels = self.surrogate.predict().detach().argmax(1)
pseudo_labels[idx_train] = labels[idx_train]
modified_adj.requires_grad = True
for i in range(n_perturbations):
adj_norm = utils.normalize_adj_tensor(modified_adj)
if self.attack_structure:
output = self.surrogate(modified_features, adj_norm)
loss = F.nll_loss(output[[target_node]], pseudo_labels[[target_node]])
grad = torch.autograd.grad(loss, modified_adj)[0]
# bidirection
grad = (grad[target_node] + grad[:, target_node]) * (-2*modified_adj[target_node] + 1)
grad[target_node] = -10
grad_argmax = torch.argmax(grad)
value = -2*modified_adj[target_node][grad_argmax] + 1
modified_adj.data[target_node][grad_argmax] += value
modified_adj.data[grad_argmax][target_node] += value
if self.attack_features:
pass
modified_adj = modified_adj.detach().cpu().numpy()
modified_adj = sp.csr_matrix(modified_adj)
self.check_adj(modified_adj)
self.modified_adj = modified_adj
def attack(self, ori_features, ori_adj, labels, idx_train, target_node,
n_perturbations, **kwargs):
"""Generate perturbations on the input graph.
Parameters
----------
ori_features : scipy.sparse.csr_matrix
Original (unperturbed) adjacency matrix
ori_adj : scipy.sparse.csr_matrix
Original (unperturbed) node feature matrix
labels :
node labels
idx_train :
node training indices
target_node : int
target node index to be attacked
n_perturbations : int
Number of perturbations on the input graph. Perturbations could
be edge removals/additions or feature removals/additions.
"""
modified_adj = ori_adj.todense()
modified_features = ori_features.todense()
modified_adj, modified_features, labels = utils.to_tensor(
modified_adj, modified_features, labels, device=self.device)
self.surrogate.eval()
print('number of pertubations: %s' % n_perturbations)
for i in range(n_perturbations):
modified_row = modified_adj[target_node] + self.adj_changes
modified_adj[target_node] = modified_row
adj_norm = utils.normalize_adj_tensor(modified_adj)
if self.attack_structure:
output = self.surrogate(modified_features, adj_norm)
loss = F.nll_loss(output[idx_train], labels[idx_train])
# acc_train = accuracy(output[idx_train], labels[idx_train])
grad = torch.autograd.grad(loss,
self.adj_changes,
retain_graph=True)[0]
grad = grad * (-2 * modified_row + 1)
grad[target_node] = 0
grad_argmax = torch.argmax(grad)
value = -2 * modified_row[grad_argmax] + 1
modified_adj.data[target_node][grad_argmax] += value
modified_adj.data[grad_argmax][target_node] += value
if self.attack_features:
pass
modified_adj = modified_adj.detach().cpu().numpy()
modified_adj = sp.csr_matrix(modified_adj)
self.check_adj(modified_adj)
self.modified_adj = modified_adj
def fit(self,
features,
adj,
labels,
idx_train,
idx_val=None,
train_iters=200,
initialize=True,
verbose=True,
normalize=True,
patience=1000,
adj2=None):
'''
train the gcn model, when idx_val is not None, pick the best model
according to the validation loss
'''
self.device = self.gc1.weight_x.weight.device
#if initialize:
# self.initialize()
if type(adj) is not torch.Tensor:
features, adj, labels = utils.to_tensor(features,
adj,
labels,
device=self.device)
else:
features = features.to(self.device)
adj = adj.to(self.device)
labels = labels.to(self.device)
if normalize:
if utils.is_sparse_tensor(adj):
adj_norm = utils.normalize_adj_tensor(adj, sparse=True)
else:
adj_norm = utils.normalize_adj_tensor(adj)
else:
adj_norm = adj
adj_norm = adj_norm * (1 - torch.eye(adj_norm.size(0)).cuda())
adj_norm = self.normalize_adj(adj_norm)
self.adj_norm_dense = adj_norm
adj_norm = to_sparse(adj_norm)
self.adj_norm = adj_norm
self.features = features
self.labels = labels
if idx_val is None:
self._train_without_val(labels, idx_train, train_iters, verbose)
else:
if patience < train_iters:
self._train_with_early_stopping(labels, idx_train, idx_val,
train_iters, patience, verbose)
else:
self._train_with_val(labels, idx_train, idx_val, train_iters,
verbose)
def attack(self, ori_features, ori_adj, labels, idx_train, n_perturbations, epochs=200, **kwargs):
"""
Generate perturbations on the input graph
"""
victim_model = self.surrogate
self.sparse_features = sp.issparse(ori_features)
ori_adj, ori_features, labels = utils.to_tensor(ori_adj, ori_features, labels, device=self.device)
victim_model.eval()
for t in tqdm(range(epochs)):
modified_adj = self.get_modified_adj(ori_adj)
adj_norm = utils.normalize_adj_tensor(modified_adj, device=self.device)
output = victim_model(ori_features, adj_norm)
self.loss = self._loss(output[idx_train], labels[idx_train])
# New: add regularization term for spectral distance
if self.regularization_weight != 0:
ori_adj_norm = utils.normalize_adj_tensor(ori_adj, device=self.device)
ori_e, ori_v = torch.symeig(ori_adj_norm, eigenvectors=True)
e, v = torch.symeig(adj_norm, eigenvectors=True)
self.regularization = F.mse_loss(ori_e, e)
self.norm = torch.norm(ori_e)
self.loss += self.regularization / self.norm * self.regularization_weight
adj_grad = torch.autograd.grad(self.loss, self.adj_changes)[0]
if self.loss_type == 'CE':
lr = 200 / np.sqrt(t+1)
self.adj_changes.data.add_(lr * adj_grad)
if self.loss_type == 'CW':
lr = 0.1 / np.sqrt(t+1)
self.adj_changes.data.add_(lr * adj_grad)
self.projection(n_perturbations)
self.random_sample(ori_adj, ori_features, labels, idx_train, n_perturbations)
self.modified_adj = self.get_modified_adj(ori_adj).detach()
self.check_adj_tensor(self.modified_adj)
# for sanity check
ori_adj_norm = utils.normalize_adj_tensor(ori_adj, device=self.device)
ori_e, ori_v = torch.symeig(ori_adj_norm, eigenvectors=True)
adj_norm = utils.normalize_adj_tensor(self.modified_adj, device=self.device)
e, v = torch.symeig(adj_norm, eigenvectors=True)
self.adj = ori_adj.detach()
self.labels = labels.detach()
self.ori_e = ori_e
self.ori_v = ori_v
self.e = e
self.v = v
def attack(self, ori_features, ori_adj, labels, idx_train,
n_perturbations):
victim_model = self.surrogate
self.sparse_features = sp.issparse(ori_features)
ori_adj, ori_features, labels = utils.to_tensor(ori_adj,
ori_features,
labels,
device=self.device)
# optimizer
optimizer = optim.Adam(victim_model.parameters(), lr=0.01)
epochs = 200
victim_model.eval()
for t in tqdm(range(epochs)):
# update victim model
victim_model.train()
modified_adj = self.get_modified_adj(ori_adj)
adj_norm = utils.normalize_adj_tensor(modified_adj)
output = victim_model(ori_features, adj_norm)
loss = self._loss(output[idx_train], labels[idx_train])
optimizer.zero_grad()
loss.backward()
optimizer.step()
# generate pgd attack
victim_model.eval()
modified_adj = self.get_modified_adj(ori_adj)
adj_norm = utils.normalize_adj_tensor(modified_adj)
output = victim_model(ori_features, adj_norm)
loss = self._loss(output[idx_train], labels[idx_train])
adj_grad = torch.autograd.grad(loss, self.adj_changes)[0]
# adj_grad = self.adj_changes.grad
if self.loss_type == 'CE':
lr = 200 / np.sqrt(t + 1)
self.adj_changes.data.add_(lr * adj_grad)
if self.loss_type == 'CW':
lr = 0.1 / np.sqrt(t + 1)
self.adj_changes.data.add_(lr * adj_grad)
# self.adj_changes.grad.zero_()
self.projection(n_perturbations)
self.random_sample(ori_adj, ori_features, labels, idx_train,
n_perturbations)
self.modified_adj = self.get_modified_adj(ori_adj).detach()
def predict(self, features=None, adj=None):
'''By default, inputs are unnormalized data'''
self.eval()
if features is None and adj is None:
return self.forward(self.features, self.adj_norm)
else:
if type(adj) is not torch.Tensor:
features, adj = utils.to_tensor(features, adj, device=self.device)
self.features = features
if utils.is_sparse_tensor(adj):
self.adj_norm = utils.normalize_adj_tensor(adj, sparse=True)
else:
self.adj_norm = utils.normalize_adj_tensor(adj)
return self.forward(self.features, self.adj_norm)
def predict(self, features=None, adj=None):
'''By default, inputs are unnormalized data'''
self.eval()
if features is None and adj is None:
return self.forward()
else:
if type(adj) is not torch.Tensor:
adj, features = utils.to_tensor(adj.todense(),
features.todense(),
device=self.device)
self.features = features
self.adj_norm1 = self._normalize_adj(adj, power=-1 / 2)
self.adj_norm2 = self._normalize_adj(adj, power=-1)
return self.forward()
def fit(self,
features,
adj,
labels,
idx_train,
idx_val=None,
train_iters=200,
verbose=True,
**kwargs):
"""Train RGCN.
Parameters
----------
features :
node features
adj :
the adjacency matrix. The format could be torch.tensor or scipy matrix
labels :
node labels
idx_train :
node training indices
idx_val :
node validation indices. If not given (None), GCN training process will not adpot early stopping
train_iters : int
number of training epochs
verbose : bool
whether to show verbose logs
"""
adj, features, labels = utils.to_tensor(adj.todense(),
features.todense(),
labels,
device=self.device)
self.features, self.labels = features, labels
self.adj_norm1 = self._normalize_adj(adj, power=-1 / 2)
self.adj_norm2 = self._normalize_adj(adj, power=-1)
print('=== training rgcn model ===')
self._initialize()
if idx_val is None:
self._train_without_val(labels, idx_train, train_iters, verbose)
else:
self._train_with_val(labels, idx_train, idx_val, train_iters,
verbose)
def attack(self,
ori_features,
ori_adj,
labels,
idx_train,
target_node,
n_perturbations,
steps=10):
self.surrogate.eval()
self.target_node = target_node
modified_adj = ori_adj.todense()
modified_features = ori_features.todense()
adj, features, labels = utils.to_tensor(modified_adj,
modified_features,
labels,
device=self.device)
adj_norm = utils.normalize_adj_tensor(adj)
s_e = np.zeros(adj.shape[1])
s_f = np.zeros(features.shape[1])
if self.attack_structure:
s_e = self.calc_importance_edge(features, adj_norm, labels,
idx_train, steps)
if self.attack_features:
s_f = self.calc_importance_feature(features, adj_norm, labels,
idx_train, steps)
for t in (range(n_perturbations)):
s_e_max = np.argmax(s_e)
s_f_max = np.argmax(s_f)
if s_e[s_e_max] >= s_f[s_f_max]:
value = np.abs(1 - modified_adj[target_node, s_e_max])
modified_adj[target_node, s_e_max] = value
modified_adj[s_e_max, target_node] = value
s_e[s_e_max] = 0
else:
modified_features[target_node, s_f_max] = np.abs(
1 - modified_features[target_node, s_f_max])
s_f[s_f_max] = 0
self.modified_adj = sp.csr_matrix(modified_adj)
self.modified_features = sp.csr_matrix(modified_features)
self.check_adj(modified_adj)
def attack(self, ori_features, ori_adj, labels, idx_train, perturbations):
victim_model = self.surrogate
self.sparse_features = sp.issparse(ori_features)
ori_adj, ori_features, labels = utils.to_tensor(ori_adj,
ori_features,
labels,
device=self.device)
modified_adj = ori_adj
victim_model.eval()
epochs = 200
for t in tqdm(range(epochs)):
modified_adj = self.get_modified_adj(ori_adj)
adj_norm = utils.normalize_adj_tensor(modified_adj)
output = victim_model(ori_features, adj_norm)
loss = F.nll_loss(output[idx_train], labels[idx_train])
adj_grad = torch.autograd.grad(loss, self.adj_changes)[0]
lr = 200 / np.sqrt(t + 1)
self.adj_changes.data.add_(lr * adj_grad)
self.projection(perturbations)
K = 20
best_loss = 0
with torch.no_grad():
s = self.adj_changes.cpu().numpy()
for i in range(K):
sampled = np.random.binomial(1, s)
print(sampled.sum())
if sampled.sum() > perturbations:
continue
self.adj_changes.data.copy_(torch.tensor(sampled))
modified_adj = self.get_modified_adj(ori_adj)
adj_norm = utils.normalize_adj_tensor(modified_adj)
output = victim_model(ori_features, adj_norm)
loss = F.nll_loss(output[idx_train], labels[idx_train])
print(loss)
if best_loss < loss:
best_loss = loss
best_s = sampled
self.adj_changes.data.copy_(torch.tensor(best_s))
self.modified_adj = self.get_modified_adj(ori_adj).detach()
def fit(self,
features,
adj,
labels,
idx_train,
idx_val=None,
idx_test=None,
k=10,
train_iters=200,
initialize=True,
verbose=True,
attention=None):
# try k=10, maybe it works better
print('runing SVD')
modified_adj = self.truncatedSVD(adj, k=k)
"""discard the edges lower than threshold, and set the residual as 1"""
threshold = 0.2
modified_adj[modified_adj < threshold] = 0
modified_adj[modified_adj >= threshold] = 1
id = modified_adj >= threshold
print('Kept {} edges'.format(id.sum()))
modified_adj = scipy.sparse.lil_matrix(
modified_adj) # change the dense tensor to lil sparse
# modified_adj_tensor = utils.sparse_mx_to_torch_sparse_tensor(self.modified_adj)
features, modified_adj, labels = utils.to_tensor(features,
modified_adj,
labels,
device=self.device)
self.modified_adj = modified_adj
self.features = features
self.labels = labels
super().fit(features,
modified_adj,
labels,
idx_train,
idx_val,
idx_test=None,
train_iters=train_iters,
initialize=initialize,
verbose=verbose)
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, features, adj, labels, idx_train, target_node,
n_perturbations):
# adj: sp.csr_matrix
modified_adj = adj.todense()
features = features.todense()
modified_adj, features, labels = utils.to_tensor(modified_adj,
features,
labels,
device=self.device)
self.surrogate.eval()
print('number of pertubations: %s' % n_perturbations)
for i in range(n_perturbations):
modified_row = modified_adj[target_node] + self.adj_changes
modified_adj[target_node] = modified_row
adj_norm = utils.normalize_adj_tensor(modified_adj)
if self.attack_structure:
output = self.surrogate(features, adj_norm)
loss = F.nll_loss(output[idx_train], labels[idx_train])
# acc_train = accuracy(output[idx_train], labels[idx_train])
grad = torch.autograd.grad(loss,
self.adj_changes,
retain_graph=True)[0]
grad = grad * (-2 * modified_row + 1)
grad[target_node] = 0
grad_argmax = torch.argmax(grad)
value = -2 * modified_row[grad_argmax] + 1
modified_adj.data[target_node][grad_argmax] += value
modified_adj.data[grad_argmax][target_node] += value
if self.attack_features:
pass
modified_adj = modified_adj.detach().cpu().numpy()
modified_adj = sp.csr_matrix(modified_adj)
self.check_adj(modified_adj)
self.modified_adj = modified_adj
def fit(self,
features,
adj,
labels,
idx_train,
idx_val=None,
train_iters=200,
initialize=True,
verbose=False,
normalize=True,
patience=500,
**kwargs):
"""Train the gcn model, when idx_val is not None, pick the best model according to the validation loss.
Parameters
----------
features :
node features
adj :
the adjacency matrix. The format could be torch.tensor or scipy matrix
labels :
node labels
idx_train :
node training indices
idx_val :
node validation indices. If not given (None), GCN training process will not adpot early stopping
train_iters : int
number of training epochs
initialize : bool
whether to initialize parameters before training
verbose : bool
whether to show verbose logs
normalize : bool
whether to normalize the input adjacency matrix.
patience : int
patience for early stopping, only valid when `idx_val` is given
"""
self.device = self.gc1.weight.device
if initialize:
self.initialize()
if type(adj) is not torch.Tensor:
features, adj, labels = utils.to_tensor(features,
adj,
labels,
device=self.device)
else:
features = features.to(self.device)
adj = adj.to(self.device)
labels = labels.to(self.device)
if normalize:
if utils.is_sparse_tensor(adj):
adj_norm = utils.normalize_adj_tensor(adj, sparse=True)
else:
adj_norm = utils.normalize_adj_tensor(adj)
else:
adj_norm = adj
self.adj_norm = adj_norm
self.features = features
self.labels = labels
if idx_val is None:
self._train_without_val(labels, idx_train, train_iters, verbose)
else:
if patience < train_iters:
self._train_with_early_stopping(labels, idx_train, idx_val,
train_iters, patience, verbose)
else:
self._train_with_val(labels, idx_train, idx_val, train_iters,
verbose)
def fit(
self,
features,
adj,
labels,
idx_train,
idx_val=None,
idx_test=None,
train_iters=81,
att_0=None,
attention=False,
model_name=None,
initialize=True,
verbose=False,
normalize=False,
patience=510,
):
'''
train the gcn model, when idx_val is not None, pick the best model
according to the validation loss
'''
"""SAINT Sampler"""
"""form data"""
data = Data(adj=adj,
features=features.to_dense(),
labels=labels,
idx_train=idx_train,
idx_val=idx_val,
idx_test=idx_test,
num_node_features=int(features.shape[-1]),
num_classes=int(labels.max() + 1))
data.num_nodes = 2110
data.num_classes = int(labels.max() + 1)
data.num_node_features = int(features.shape[-1])
# loader = GraphSAINTRandomWalkSampler(data, batch_size=6000, walk_length=2,
# num_steps=5, sample_coverage=1000,
# save_dir='saint_data/',
# num_workers=1)
self.sim = None
self.idx_test = idx_test
self.attention = attention
if initialize:
self.initialize()
if type(adj) is not torch.Tensor:
features, adj, labels = utils.to_tensor(features,
adj,
labels,
device=self.device)
else:
features = features.to(self.device)
adj = adj.to(self.device)
labels = labels.to(self.device)
# normalize = False # we don't need normalize here, the norm is conducted in the GCN (self.gcn1) model
# if normalize:
# if utils.is_sparse_tensor(adj):
# adj_norm = utils.normalize_adj_tensor(adj, sparse=True)
# else:
# adj_norm = utils.normalize_adj_tensor(adj)
# else:
# adj_norm = adj
# add self loop
adj = self.add_loop_sparse(adj)
"""The normalization gonna be done in the GCNConv"""
self.adj_norm = adj
self.features = features
self.labels = labels
# if idx_val is None:
# self._train_without_val(labels, idx_train, train_iters, verbose)
# else:
# if patience < train_iters:
# self._train_with_early_stopping(labels, idx_train, idx_val, train_iters, patience, verbose)
# else:
self._train_with_val(labels, idx_train, idx_val, train_iters, verbose)
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()
def attack(self,
ori_features,
ori_adj,
labels,
idx_train,
target_node,
n_perturbations,
steps=10,
**kwargs):
"""Generate 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
target_node : int
target node index to be attacked
n_perturbations : int
Number of perturbations on the input graph. Perturbations could
be edge removals/additions or feature removals/additions.
steps : int
steps for computing integrated gradients
"""
self.surrogate.eval()
self.target_node = target_node
modified_adj = ori_adj.todense()
modified_features = ori_features.todense()
adj, features, labels = utils.to_tensor(modified_adj,
modified_features,
labels,
device=self.device)
adj_norm = utils.normalize_adj_tensor(adj)
s_e = np.zeros(adj.shape[1])
s_f = np.zeros(features.shape[1])
if self.attack_structure:
s_e = self.calc_importance_edge(features, adj_norm, labels,
idx_train, steps)
if self.attack_features:
s_f = self.calc_importance_feature(features, adj_norm, labels,
idx_train, steps)
for t in (range(n_perturbations)):
s_e_max = np.argmax(s_e)
s_f_max = np.argmax(s_f)
if s_e[s_e_max] >= s_f[s_f_max]:
value = np.abs(1 - modified_adj[target_node, s_e_max])
modified_adj[target_node, s_e_max] = value
modified_adj[s_e_max, target_node] = value
s_e[s_e_max] = 0
else:
modified_features[target_node, s_f_max] = np.abs(
1 - modified_features[target_node, s_f_max])
s_f[s_f_max] = 0
self.modified_adj = sp.csr_matrix(modified_adj)
self.modified_features = sp.csr_matrix(modified_features)
self.check_adj(modified_adj)
def fit(
self,
features,
adj,
labels,
idx_train,
idx_val=None,
idx_test=None,
train_iters=101,
att_0=None,
attention=False,
model_name=None,
initialize=True,
verbose=False,
normalize=False,
patience=500,
):
'''
train the gcn model, when idx_val is not None, pick the best model
according to the validation loss
'''
self.sim = None
self.attention = attention
if self.attention:
att_0 = att_coef(features, adj)
adj = att_0 # update adj
self.sim = att_0 # update att_0
self.idx_test = idx_test
# self.model_name = model_name
# self.device = self.gc1.weight.device
if initialize:
self.initialize()
if type(adj) is not torch.Tensor:
features, adj, labels = utils.to_tensor(features,
adj,
labels,
device=self.device)
else:
features = features.to(self.device)
adj = adj.to(self.device)
labels = labels.to(self.device)
normalize = False # we don't need normalize here, the norm is conducted in the GCN (self.gcn1) model
if normalize:
if utils.is_sparse_tensor(adj):
adj_norm = utils.normalize_adj_tensor(adj, sparse=True)
else:
adj_norm = utils.normalize_adj_tensor(adj)
else:
adj_norm = adj
"""Make the coefficient D^{-1/2}(A+I)D^{-1/2}"""
self.adj_norm = adj_norm
self.features = features
self.labels = labels
if idx_val is None:
self._train_without_val(labels, idx_train, train_iters, verbose)
else:
if patience < train_iters:
self._train_with_early_stopping(labels, idx_train, idx_val,
train_iters, patience, verbose)
else:
self._train_with_val(labels, idx_train, idx_val, train_iters,
verbose)
def fit(self,
features,
adj,
labels,
idx_train,
idx_val=None,
train_iters=200,
verbose=True,
**kwargs):
"""Train RGCN.
Parameters
----------
features :
node features
adj :
the adjacency matrix. The format could be torch.tensor or scipy matrix
labels :
node labels
idx_train :
node training indices
idx_val :
node validation indices. If not given (None), GCN training process will not adpot early stopping
train_iters : int
number of training epochs
verbose : bool
whether to show verbose logs
Examples
--------
We can first load dataset and then train RGCN.
>>> from deeprobust.graph.data import PrePtbDataset, Dataset
>>> from deeprobust.graph.defense import RGCN
>>> # load clean graph data
>>> data = Dataset(root='/tmp/', name='cora', seed=15)
>>> adj, features, labels = data.adj, data.features, data.labels
>>> idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
>>> # load perturbed graph data
>>> perturbed_data = PrePtbDataset(root='/tmp/', name='cora')
>>> perturbed_adj = perturbed_data.adj
>>> # train defense model
>>> model = RGCN(nnodes=perturbed_adj.shape[0], nfeat=features.shape[1],
nclass=labels.max()+1, nhid=32, device='cpu')
>>> model.fit(features, perturbed_adj, labels, idx_train, idx_val,
train_iters=200, verbose=True)
>>> model.test(idx_test)
"""
adj, features, labels = utils.to_tensor(adj.todense(),
features.todense(),
labels,
device=self.device)
self.features, self.labels = features, labels
self.adj_norm1 = self._normalize_adj(adj, power=-1 / 2)
self.adj_norm2 = self._normalize_adj(adj, power=-1)
print('=== training rgcn model ===')
self._initialize()
if idx_val is None:
self._train_without_val(labels, idx_train, train_iters, verbose)
else:
self._train_with_val(labels, idx_train, idx_val, train_iters,
verbose)
