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# compute cgscore for gcn
# author: Yaning
import torch
import numpy as np
import torch.nn.functional as Fd
from deeprobust.graph.defense import GCNJaccard, GCN
from deeprobust.graph.defense import GCNScore
from deeprobust.graph.utils import *
from deeprobust.graph.data import Dataset, PrePtbDataset
from scipy.sparse import csr_matrix
import argparse
import pickle
from deeprobust.graph import utils
from collections import defaultdict
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=15, help='Random seed.')
parser.add_argument('--dataset', type=str, default='polblogs', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed'], help='dataset')
parser.add_argument('--ptb_rate', type=float, default=0.10, help='pertubation rate')
args = parser.parse_args()
args.cuda = torch.cuda.is_available()
print('cuda: %s' % args.cuda)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# make sure you use the same data splits as you generated attacks
np.random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
# Here the random seed is to split the train/val/test data,
# we need to set the random seed to be the same as that when you generate the perturbed graph
# data = Dataset(root='/tmp/', name=args.dataset, setting='nettack', seed=15)
# Or we can just use setting='prognn' to get the splits
data = Dataset(root='/tmp/', name=args.dataset, setting='prognn')
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
perturbed_data = PrePtbDataset(root='/tmp/',
name=args.dataset,
attack_method='meta',
ptb_rate=args.ptb_rate)
perturbed_adj = perturbed_data.adj
# perturbed_adj = adj
def save_cg_scores(cg_scores, filename="cg_scores.npy"):
np.save(filename, cg_scores)
print(f"CG-scores saved to {filename}")
def load_cg_scores_numpy(filename="cg_scores.npy"):
cg_scores = np.load(filename, allow_pickle=True)
print(f"CG-scores loaded from {filename}")
return cg_scores
def calc_cg_score_gnn_with_sampling(
A, X, labels, device, rep_num=1, unbalance_ratio=1, sub_term=False
):
"""
Calculate CG-score for each edge in a graph with node labels and random sampling.
Args:
A: torch.Tensor
Adjacency matrix of the graph (size: N x N).
X: torch.Tensor
Node features matrix (size: N x F).
labels: torch.Tensor
Node labels (size: N).
device: torch.device
Device to perform calculations.
rep_num: int
Number of repetitions for Monte Carlo sampling.
unbalance_ratio: float
Ratio of unbalanced data (1:unbalance_ratio).
sub_term: bool
If True, calculate and return sub-terms.
Returns:
cg_scores: dict
Dictionary containing CG-scores for edges and optionally sub-terms.
"""
N = A.shape[0]
cg_scores = {
"vi": np.zeros((N, N)),
"ab": np.zeros((N, N)),
"a2": np.zeros((N, N)),
"b2": np.zeros((N, N)),
"times": np.zeros((N, N)),
}
with torch.no_grad():
for _ in range(rep_num):
# Compute AX (node representations)
AX = torch.matmul(A, X).to(device)
norm_AX = AX / torch.norm(AX, dim=1, keepdim=True)
# Group nodes by their labels
dataset = defaultdict(list)
data_idx = defaultdict(list)
for i, label in enumerate(labels):
dataset[label.item()].append(norm_AX[i].unsqueeze(0)) # Store normalized data
data_idx[label.item()].append(i) # Store indices
# Convert to tensors
for label, data_list in dataset.items():
dataset[label] = torch.cat(data_list, dim=0)
data_idx[label] = torch.tensor(data_idx[label], dtype=torch.long, device=device)
# Calculate CG-scores for each label group
for curr_label, curr_samples in dataset.items():
curr_indices = data_idx[curr_label]
curr_num = len(curr_samples)
# Randomly sample a subset of current label examples
chosen_curr_idx = np.random.choice(range(curr_num), curr_num, replace=False)
chosen_curr_samples = curr_samples[chosen_curr_idx]
chosen_curr_indices = curr_indices[chosen_curr_idx]
# Sample negative examples from other classes
neg_samples = torch.cat(
[dataset[l] for l in dataset if l != curr_label], dim=0
)
neg_indices = torch.cat(
[data_idx[l] for l in data_idx if l != curr_label], dim=0
)
neg_num = min(int(curr_num * unbalance_ratio), len(neg_samples))
chosen_neg_samples = neg_samples[
torch.randperm(len(neg_samples))[:neg_num]
]
# Combine positive and negative samples
combined_samples = torch.cat([chosen_curr_samples, chosen_neg_samples], dim=0)
y = torch.cat(
[torch.ones(len(chosen_curr_samples)), -torch.ones(neg_num)], dim=0
).to(device)
# Compute the Gram matrix H^\infty
H_inner = torch.matmul(combined_samples, combined_samples.T)
del combined_samples
###
H_inner = torch.clamp(H_inner, min=-1.0, max=1.0)
###
H = H_inner * (np.pi - torch.acos(H_inner)) / (2 * np.pi)
del H_inner
H.fill_diagonal_(0.5)
##
epsilon = 1e-6
H = H + epsilon * torch.eye(H.size(0), device=H.device)
##
invH = torch.inverse(H)
del H
original_error = y @ (invH @ y)
# Compute CG-scores for each edge
for i in chosen_curr_indices:
print("the node index:", i)
for j in range(i + 1, N): # Upper triangular traversal
# print(j)
if A[i, j] == 0: # Skip if no edge exists
continue
# Remove edge (i, j) to create A1
A1 = A.clone()
A1[i, j] = A1[j, i] = 0
# Recompute AX with A1
AX1 = torch.matmul(A1, X).to(device)
norm_AX1 = AX1 / torch.norm(AX1, dim=1, keepdim=True)
# Repeat error calculation with A1
curr_samples_A1 = norm_AX1[chosen_curr_indices]
neg_samples_A1 = norm_AX1[neg_indices]
chosen_neg_samples_A1 = neg_samples_A1[
torch.randperm(len(neg_samples_A1))[:neg_num]
]
combined_samples_A1 = torch.cat(
[curr_samples_A1, chosen_neg_samples_A1], dim=0
)
H_inner_A1 = torch.matmul(combined_samples_A1, combined_samples_A1.T)
del combined_samples_A1
### trick1
H_inner_A1 = torch.clamp(H_inner_A1, min=-1.0, max=1.0)
###
H_A1 = H_inner_A1 * (np.pi - torch.acos(H_inner_A1)) / (2 * np.pi)
del H_inner_A1
H_A1.fill_diagonal_(0.5)
### trick2
epsilon = 1e-6
H_A1= H_A1 + epsilon * torch.eye(H_A1.size(0), device=H_A1.device)
###
invH_A1 = torch.inverse(H_A1)
del H_A1
error_A1 = y @ (invH_A1 @ y)
print("i:", i)
print("j:", j)
print("current score:", (original_error - error_A1).item())
# Compute the difference in error (CG-score)
cg_scores["vi"][i, j] += (original_error - error_A1).item()
cg_scores["vi"][j, i] = cg_scores["vi"][i, j] # Symmetric
cg_scores["times"][i, j] += 1
cg_scores["times"][j, i] += 1
# Normalize CG-scores by repetition count
for key, values in cg_scores.items():
if key == "times":
continue
cg_scores[key] = values / np.where(cg_scores["times"] > 0, cg_scores["times"], 1)
return cg_scores if sub_term else cg_scores["vi"]
def is_symmetric_sparse(adj):
"""
Check if a sparse matrix is symmetric.
"""
# Check symmetry
return (adj != adj.transpose()).nnz == 0 # .nnz is the number of non-zero elements
def make_symmetric_sparse(adj):
"""
Ensure the sparse adjacency matrix is symmetrical.
"""
# Make the matrix symmetric
sym_adj = (adj + adj.transpose()) / 2
return sym_adj
perturbed_adj = make_symmetric_sparse(perturbed_adj)
if type(perturbed_adj) is not torch.Tensor:
features, perturbed_adj, labels = utils.to_tensor(features, perturbed_adj, labels)
else:
features = features.to(device)
perturbed_adj = perturbed_adj.to(device)
labels = labels.to(device)
if utils.is_sparse_tensor(perturbed_adj):
adj_norm = utils.normalize_adj_tensor(perturbed_adj, sparse=True)
else:
adj_norm = utils.normalize_adj_tensor(perturbed_adj)
features = features.to_dense()
perturbed_adj = adj_norm.to_dense()
calc_cg_score = calc_cg_score_gnn_with_sampling(perturbed_adj, features, labels, device, rep_num=1, unbalance_ratio=1, sub_term=False)
save_cg_scores(calc_cg_score, filename="cg_scores_polblogs_0.10.npy")
# print("completed"）
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