# 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 from tqdm import tqdm parser = argparse.ArgumentParser() parser.add_argument('--seed', type=int, default=15, help='Random seed.') parser.add_argument('--dataset', type=str, default='pubmed', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed'], help='dataset') parser.add_argument('--ptb_rate', type=float, default=0.05, help='pertubation rate') args = parser.parse_args() args.cuda = torch.cuda.is_available() print('cuda: %s' % args.cuda) device = torch.device("cuda:1" 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 import torch import numpy as np from collections import defaultdict from tqdm import tqdm def calc_cg_score_gnn_with_sampling( A, X, labels, device, rep_num=1, unbalance_ratio=1, sub_term=False, batch_size=64 ): """ Optimized CG-score calculation with edge batching and GPU acceleration. """ 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)), } A = A.to(device) X = X.to(device) labels = labels.to(device) @torch.no_grad() def normalize(tensor): return tensor / (torch.norm(tensor, dim=1, keepdim=True) + 1e-8) for _ in range(rep_num): AX = torch.matmul(A, X) norm_AX = normalize(AX) # ✨ Step 1: 标签分组（矢量化 + GPU） unique_labels = torch.unique(labels) label_to_indices = { label.item(): (labels == label).nonzero(as_tuple=True)[0] for label in unique_labels } dataset = {label: norm_AX[indices] for label, indices in label_to_indices.items()} # ✨ Step 2: 负样本构建（GPU 上） neg_samples_dict = {} neg_indices_dict = {} for label in unique_labels: label = label.item() mask = labels != label neg_samples = norm_AX[mask] neg_indices = mask.nonzero(as_tuple=True)[0] neg_samples_dict[label] = neg_samples neg_indices_dict[label] = neg_indices for curr_label in tqdm(unique_labels.tolist(), desc="Label groups"): curr_samples = dataset[curr_label] curr_indices = label_to_indices[curr_label] curr_num = len(curr_samples) chosen_curr_idx = torch.randperm(curr_num, device=device) chosen_curr_samples = curr_samples[chosen_curr_idx] chosen_curr_indices = curr_indices[chosen_curr_idx] neg_samples = neg_samples_dict[curr_label] neg_indices = neg_indices_dict[curr_label] neg_num = min(int(curr_num * unbalance_ratio), len(neg_samples)) rand_idx = torch.randperm(len(neg_samples), device=device)[:neg_num] chosen_neg_samples = neg_samples[rand_idx] chosen_neg_indices = neg_indices[rand_idx] 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) # 参考误差 H_inner = torch.matmul(combined_samples, combined_samples.T) H_inner = torch.clamp(H_inner, min=-1.0, max=1.0) H = H_inner * (np.pi - torch.acos(H_inner)) / (2 * np.pi) H.fill_diagonal_(0.5) H += 1e-6 * torch.eye(H.size(0), device=device) invH = torch.inverse(H) original_error = y @ (invH @ y) # ✨ Step 3: 收集候选边（仍在 CPU 逻辑） edge_batch = [] for idx_i in chosen_curr_indices.tolist(): for j in range(idx_i + 1, N): if A[idx_i, j] != 0: edge_batch.append((idx_i, j)) # ✨ Step 4: 批处理更新 for k in tqdm(range(0, len(edge_batch), batch_size), desc="Edge batches", leave=False): batch = edge_batch[k : k + batch_size] B = len(batch) norm_AX1_batch = norm_AX.repeat(B, 1, 1).clone() for b, (i, j) in enumerate(batch): AX1_i = AX[i] - A[i, j] * X[j] AX1_j = AX[j] - A[j, i] * X[i] norm_AX1_batch[b, i] = AX1_i / (torch.norm(AX1_i) + 1e-8) norm_AX1_batch[b, j] = AX1_j / (torch.norm(AX1_j) + 1e-8) sample_idx = chosen_curr_indices.tolist() + chosen_neg_indices.tolist() sample_batch = norm_AX1_batch[:, sample_idx, :] # [B, M, D] H_inner = torch.matmul(sample_batch, sample_batch.transpose(1, 2)) H_inner = torch.clamp(H_inner, min=-1.0, max=1.0) H = H_inner * (np.pi - torch.acos(H_inner)) / (2 * np.pi) eye = torch.eye(H.size(-1), device=device).unsqueeze(0).expand_as(H) H = H + 1e-6 * eye H.diagonal(dim1=-2, dim2=-1).copy_(0.5) invH = torch.inverse(H) y_expanded = y.unsqueeze(0).expand(B, -1) error_A1 = torch.einsum("bi,bij,bj->b", y_expanded, invH, y_expanded) for b, (i, j) in enumerate(batch): score = (original_error - error_A1[b]).item() cg_scores["vi"][i, j] += score cg_scores["vi"][j, i] = score cg_scores["times"][i, j] += 1 cg_scores["times"][j, i] += 1 for key in cg_scores: if key != "times": cg_scores[key] = cg_scores[key] / 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=3, sub_term=False, batch_size=512) save_cg_scores(calc_cg_score, filename="pubmed_0.05.npy") # print("completed"）