examples/graph/cgscore_datasets_batch.py

# 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"）