Introduction to Graph Defense with Examples

In this section, we introduce the graph attack algorithms provided in DeepRobust.

Test your model’s robustness on poisoned graph

DeepRobust provides a series of defense methods that aim to enhance the robustness of GNNs.

Victim Models:

Node Embedding Victim Models: (see more details here)

Defense Methods:

  1. Load pre-attacked graph data

    from deeprobust.graph.data import Dataset, PrePtbDataset
# load the prognn splits by using setting='prognn'
# because the attacked graphs are generated under prognn splits
data = Dataset(root='/tmp/', name='cora', 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
# Load meta attacked data
perturbed_data = PrePtbDataset(root='/tmp/',
                    name='cora',
                    attack_method='meta',
                    ptb_rate=0.05)
perturbed_adj = perturbed_data.adj

  2. You can also choose to load graphs attacked by nettack. See details here

    # Load nettack attacked data
perturbed_data = PrePtbDataset(root='/tmp/', name='cora',
        attack_method='nettack',
        ptb_rate=3.0) # here ptb_rate means number of perturbation per nodes
perturbed_adj = perturbed_data.adj
idx_test = perturbed_data.target_nodes

  3. Train a victim model (GCN) on clearn/poinsed graph

    from deeprobust.graph.defense import GCN
gcn = GCN(nfeat=features.shape[1],
    nhid=16,
    nclass=labels.max().item() + 1,
    dropout=0.5, device='cpu')
gcn = gcn.to('cpu')
gcn.fit(features, adj, labels, idx_train, idx_val) # train on clean graph with earlystopping
gcn.test(idx_test)

gcn.fit(features, perturbed_adj, labels, idx_train, idx_val) # train on poisoned graph
gcn.test(idx_test)

  4. Train defense models (GCN-Jaccard, RGCN, ProGNN) poinsed graph

    from deeprobust.graph.defense import GCNJaccard
model = GCNJaccard(nfeat=features.shape[1],
          nhid=16,
          nclass=labels.max().item() + 1,
          dropout=0.5, device='cpu').to('cpu')
model.fit(features, perturbed_adj, labels, idx_train, idx_val, threshold=0.03)
model.test(idx_test)

    from deeprobust.graph.defense import GCNJaccard
model = RGCN(nnodes=perturbed_adj.shape[0], nfeat=features.shape[1],
             nclass=labels.max()+1, nhid=32, device='cpu')
model.fit(features, perturbed_adj, labels, idx_train, idx_val,
          train_iters=200, verbose=True)
model.test(idx_test)

For details in training ProGNN, please refer to this page.

More Examples

More examples can be found in deeprobust.graph.defense. You can also find examples in github code examples and more details in defense table.