def attack(self,
num_budgets=0.05,
dim=32,
window_size=5,
K=100,
attack_type="add_by_remove",
structure_attack=True,
feature_attack=False):
if not attack_type in ["remove", "add", "add_by_remove"]:
raise RuntimeError(
'attack_type should be one of "remove", "add", "add_by_remove".'
)
super().attack(num_budgets, structure_attack, feature_attack)
num_budgets = self.num_budgets
adj = self.graph.adj_matrix
if attack_type.startswith("add"):
num_candidates = min(5 * num_budgets, self.num_edges)
# num_candidates = 10000
candidates = self.generate_candidates_addition(adj, num_candidates)
else:
candidates = self.generate_candidates_removal(adj)
if attack_type == "add_by_remove":
adj = gf.flip_adj(adj, candidates)
deg_matrix = sp.diags(adj.sum(1).A1, dtype=adj.dtype)
if K:
vals_org, vecs_org = sp.linalg.eigsh(adj, k=K, M=deg_matrix)
else:
vals_org, vecs_org = spl.eigh(adj.toarray(),
deg_matrix.toarray())
delta_w = 1. - 2 * adj[candidates[:, 0], candidates[:, 1]].A1
loss_for_candidates = estimate_loss_with_delta_eigenvals(
candidates, delta_w, vals_org, vecs_org, self.num_nodes, dim,
window_size)
self.adj_flips = candidates[loss_for_candidates.argsort()
[:num_budgets]]
else:
# vector indicating whether we are adding an edge (+1) or removing an edge (-1)
delta_w = 1. - 2 * adj[candidates[:, 0], candidates[:, 1]].A1
deg_matrix = sp.diags(adj.sum(1).A1, dtype=adj.dtype)
if K:
vals_org, vecs_org = sp.linalg.eigsh(adj, k=K, M=deg_matrix)
else:
vals_org, vecs_org = spl.eigh(adj.toarray(),
deg_matrix.toarray())
loss_for_candidates = estimate_loss_with_delta_eigenvals(
candidates, delta_w, vals_org, vecs_org, self.num_nodes, dim,
window_size)
self.adj_flips = candidates[loss_for_candidates.argsort()
[-num_budgets:]]
return self
def from_flips(self, **flips):
"""Return a new graph from:
'edge_flips' or 'feat_flips'
"""
allowed = ("edge_flips", "feat_flips")
g = self.copy()
for k, v in flips.items():
if v is None:
continue
if k == "edge_flips":
g.update(adj_matrix=gf.flip_adj(g.adj_matrix, v))
elif k == "feat_flips":
g.update(attr_matrix=gf.flip_attr(g.attr_matrix, v))
else:
raise ValueError(f"Unrecognized key {k}, allowed: {allowed}.")
return g
def A(self):
adj_flips = self.edge_flips
if self.modified_adj is None:
if adj_flips is not None:
self.modified_adj = gf.flip_adj(self.graph.adj_matrix,
adj_flips)
else:
self.modified_adj = self.graph.adj_matrix
adj = self.modified_adj
if gf.is_anytensor(adj):
adj = gf.tensoras(adj)
if isinstance(adj, np.ndarray):
adj = sp.csr_matrix(adj)
elif sp.isspmatrix(adj):
adj = adj.tocsr(copy=False)
else:
raise TypeError(adj)
return adj