Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision

• URL: http://arxiv.org/abs/2203.03762v1
• Date: Mon, 7 Mar 2022 22:57:43 GMT
• Title: Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision
• Authors: Jun Zhuang, Mohammad Al Hasan
• Abstract summary: Graph Convolutional Networks (GCNs) achieve extraordinary accomplishments on the node classification task. GCNs may be vulnerable to adversarial attacks on label-scarce dynamic graphs. We propose a novel Bayesian self-supervision model, namely GraphSS, to address the issue.
• Score: 5.037076816350975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, plentiful evidence illustrates that Graph Convolutional Networks (GCNs) achieve extraordinary accomplishments on the node classification task. However, GCNs may be vulnerable to adversarial attacks on label-scarce dynamic graphs. Many existing works aim to strengthen the robustness of GCNs; for instance, adversarial training is used to shield GCNs against malicious perturbations. However, these works fail on dynamic graphs for which label scarcity is a pressing issue. To overcome label scarcity, self-training attempts to iteratively assign pseudo-labels to highly confident unlabeled nodes but such attempts may suffer serious degradation under dynamic graph perturbations. In this paper, we generalize noisy supervision as a kind of self-supervised learning method and then propose a novel Bayesian self-supervision model, namely GraphSS, to address the issue. Extensive experiments demonstrate that GraphSS can not only affirmatively alert the perturbations on dynamic graphs but also effectively recover the prediction of a node classifier when the graph is under such perturbations. These two advantages prove to be generalized over three classic GCNs across five public graph datasets.

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