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import torch |
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import numpy as np |
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import torch.nn.functional as F |
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from deeprobust.graph.defense.gcn_guard import GCNGuard |
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from deeprobust.graph.utils import * |
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from deeprobust.graph.data import Dataset |
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from deeprobust.graph.data import PtbDataset, PrePtbDataset |
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import os |
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import csv |
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import argparse |
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from scipy import sparse |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--seed', type=int, default=15, help='Random seed') |
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parser.add_argument('--GNNGuard', type=bool, default=True, choices=[True, False]) |
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parser.add_argument('--dataset', type=str, default='Flickr', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset') |
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parser.add_argument('--ptb_rate', type=float, default=0.25, help='pertubation rate') |
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parser.add_argument('--ptb_type', type=str, default='minmax', choices=['clean', 'meta', 'dice', 'minmax', 'pgd', 'random'], help='attack type') |
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parser.add_argument('--gpu', type=int, default=1, help='GPU device ID (default: 0)') |
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args = parser.parse_args() |
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args.cuda = torch.cuda.is_available() |
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print('cuda: %s' % args.cuda) |
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device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu") |
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np.random.seed(args.seed) |
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torch.manual_seed(args.seed) |
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if args.cuda: |
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torch.cuda.manual_seed(args.seed) |
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data = Dataset(root='/tmp/', name=args.dataset) |
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adj, features, labels = data.adj, data.features, data.labels |
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idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test |
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ptb_path = f"../attacked_adj/{args.dataset}/{args.ptb_type}_{args.dataset}_{args.ptb_rate}.pt" |
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perturbed_adj = torch.load(ptb_path) |
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perturbed_adj = sp.csr_matrix(perturbed_adj.to('cpu').numpy()) |
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def test(adj): |
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''' testing model ''' |
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gcn = GCNGuard(nfeat=features.shape[1], nclass=labels.max().item() + 1, nhid=16, drop=True, |
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dropout=0.5, with_relu=False, with_bias=True, weight_decay=5e-4, device=device) |
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gcn = gcn.to(device) |
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gcn.fit(features, adj, labels, idx_train, train_iters=200, idx_val=idx_val, idx_test=idx_test, verbose=True, attention=args.GNNGuard) |
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gcn.eval() |
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acc_test, _ = gcn.test(idx_test) |
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return acc_test |
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def main(): |
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print('=== testing GCN on Mettacked graph ===') |
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acc = test(perturbed_adj) |
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csv_dir = "../result" |
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os.makedirs(csv_dir, exist_ok=True) |
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csv_filename = os.path.join(csv_dir, f"GNNGuard_{args.dataset}_{args.ptb_type}_{args.ptb_rate}.csv") |
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row = [f"{args.dataset} ", f" {args.ptb_type} ", f" {args.ptb_rate} ", f" {acc}"] |
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try: |
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file_exists = os.path.isfile(csv_filename) |
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with open(csv_filename, 'a', newline='') as csvfile: |
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writer = csv.writer(csvfile) |
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if not file_exists: |
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writer.writerow(["dataset ", "ptb_type ", "ptb_rate ", "accuracy"]) |
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writer.writerow(row) |
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except Exception as e: |
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print(f"[Error] Failed to write CSV: {e}") |
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if __name__ == '__main__': |
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main() |
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