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Node Embedding Attack and Defense |
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======================= |
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In this section, we introduce the node embedding attack algorithms and |
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corresponding victim models provided in DeepRobust. |
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.. contents:: |
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:local: |
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Node Embedding Attack |
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----------------------- |
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Node embedding attack aims to fool node embedding models produce bad-quality embeddings. |
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Specifically, DeepRobust provides the following node attack algorithms: |
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- :class:`deeprobust.graph.global_attack.NodeEmbeddingAttack` |
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- :class:`deeprobust.graph.global_attack.OtherNodeEmbeddingAttack` |
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They only take the adjacency matrix as input and the adjacency |
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matrix is in the format of :obj:`scipy.sparse.csr_matrix`. You can specify the attack_type |
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to either add edges or remove edges. Let's take a look at an example: |
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.. code-block:: python |
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from deeprobust.graph.data import Dataset |
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from deeprobust.graph.global_attack import NodeEmbeddingAttack |
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data = Dataset(root='/tmp/', name='cora_ml', seed=15) |
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adj, features, labels = data.adj, data.features, data.labels |
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model = NodeEmbeddingAttack() |
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model.attack(adj, attack_type="remove") |
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modified_adj = model.modified_adj |
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model.attack(adj, attack_type="remove", min_span_tree=True) |
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modified_adj = model.modified_adj |
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model.attack(adj, attack_type="add", n_candidates=10000) |
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modified_adj = model.modified_adj |
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model.attack(adj, attack_type="add_by_remove", n_candidates=10000) |
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modified_adj = model.modified_adj |
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The :obj:`OtherNodeEmbeddingAttack` contains the baseline methods reported in the paper |
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Adversarial Attacks on Node Embeddings via Graph Poisoning. Aleksandar Bojchevski and |
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Stephan Günnemann, ICML 2019. We can specify the type (chosen from |
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`["degree", "eigencentrality", "random"]`) to generate corresponding attacks. |
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.. code-block:: python |
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from deeprobust.graph.data import Dataset |
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from deeprobust.graph.global_attack import OtherNodeEmbeddingAttack |
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data = Dataset(root='/tmp/', name='cora_ml', seed=15) |
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adj, features, labels = data.adj, data.features, data.labels |
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model = OtherNodeEmbeddingAttack(type='degree') |
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model.attack(adj, attack_type="remove") |
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modified_adj = model.modified_adj |
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model = OtherNodeEmbeddingAttack(type='eigencentrality') |
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model.attack(adj, attack_type="remove") |
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modified_adj = model.modified_adj |
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model = OtherNodeEmbeddingAttack(type='random') |
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model.attack(adj, attack_type="add", n_candidates=10000) |
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modified_adj = model.modified_adj |
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Node Embedding Victim Models |
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----------------------- |
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DeepRobust provides two node embedding victim models, DeepWalk and Node2Vec: |
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- :class:`deeprobust.graph.defense.DeepWalk` |
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- :class:`deeprobust.graph.defense.Node2Vec` |
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There are three major functions in the two classes: :obj:`fit()`, :obj:`evaluate_node_classification()` |
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and :obj:`evaluate_link_prediction`. The function :obj:`fit()` will train the node embdding models |
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and store the embedding in :obj:`self.embedding`. For example, |
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.. code-block:: python |
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from deeprobust.graph.data import Dataset |
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from deeprobust.graph.defense import DeepWalk |
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from deeprobust.graph.global_attack import NodeEmbeddingAttack |
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import numpy as np |
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dataset_str = 'cora_ml' |
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data = Dataset(root='/tmp/', name=dataset_str, seed=15) |
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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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print("Test DeepWalk on clean graph") |
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model = DeepWalk(type="skipgram") |
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model.fit(adj) |
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print(model.embedding) |
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After we trained the model, we can then test its performance on node classification and link prediction: |
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.. code-block:: python |
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print("Test DeepWalk on node classification...") |
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model.evaluate_node_classification(labels, idx_train, idx_test) |
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print("Test DeepWalk on link prediciton...") |
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model.evaluate_link_prediction(adj, np.array(adj.nonzero()).T) |
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We can then test its performance on the attacked graph: |
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.. code-block:: python |
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attacker = NodeEmbeddingAttack() |
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attacker.attack(adj, attack_type="remove", n_perturbations=1000) |
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modified_adj = attacker.modified_adj |
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print("Test DeepWalk on attacked graph") |
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model.fit(modified_adj) |
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model.evaluate_node_classification(labels, idx_train, idx_test) |
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