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MD-GNN: A Lightweight Metric Defence Strategy Against Adversarial Attacks

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MD-GNN

This repository contains the implementation of our paper:A Lightweight Metric Defence Strategy for Graph Neural Networks Against Poisoning Attacks accepted by ICICS2021.

Requirement

  • matplotlib == 3.2.2

  • torch_geometric == 1.5.0

  • networkx == 2.4

  • tqdm == 4.46.1

  • torch == 1.5.0

  • seaborn == 0.10.1

  • scipy == 1.4.1

  • numpy == 1.18.1

  • openpyxl == 3.0.6

  • deeprobust == 0.2.1

  • scikit_learn==0.24.2

Run Test

  • run test

    python test.py
  • run MD-GCN

    # Supported Datasets:['cora', 'citeseer', 'cora_ml']
    # Supported Attacks:['meta', 'nettack', 'random']
    # Supported Metrics:['Cfs', 'Cfs1', 'Cfs2', 'Cfs3', 'Cfs4', 'Cs', 'Cs1', 'Jaccard1']
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    data = Dataset(root='data/', name='cora', seed=15, require_mask=True)
    data.adj = sp.load_npz('adv_adj/cora_meta_adj_0.25.npz')
    output, time = CFS1(data, device, 'Cfs')
  • run experiments

    from src.generate_experiment_data import *
    from src.generate_experiment_figure import *
    from src.save_excel import *
    
    dataset_list = ['cora', 'citeseer', 'cora_ml']
    attack_list = ['meta', 'nettack', 'random']
    defense_list = ['GCN', 'GCNSVD', 'Jaccard', 'ProGNN', 'RGCN', 'GAT', 'CFS', 'CfsGAT', 'CfsRGCN', 'GNNGuard', 'HGCN', 'CfsHGCN']
    root = 'data/'
    root1 = 'adv_adj/'
    save_dir = 'experiment/data/0/'
    
    threshold_list = [0.03, 0.05]
    a = [0.05, 0.1]
    lr_list = [0.001, 0.005, 0.01, 0.05, 0.1]
    
    # run experiments
    experiment1(dataset_list, attack_list,defense_list, root, root1, save_dir)
    experiment2(dataset_list, attack_list, threshold_list, root, root1, save_dir)
    experiment3(dataset_list, attack_list, root, root1, save_dir, a)
    experiment4(dataset_list, attack_list, defense_list, root, root1, save_dir, 'Cfs4')
    experiment5(dataset_list, attack_list, lr_list, root, root1, save_dir, 'Cfs', 10)
    
    # generate experimental results
    figure1()
    figure2()
    figure3()
    figure4()
    
    result()
    result_time()
  • others

    run generate_adv.py # generate adversarial attacks
    run generate_clean.py # generate pre-processed graph

Project Structure

  • MD-GNN
    • adv-adj
    • data
    • defense
    • experiment
    • src:source code
    • generate_adv.py
    • generate_clean.py
    • test.py
    • utils.py

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MD-GNN: A Lightweight Metric Defence Strategy Against Adversarial Attacks

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