Zhuang, JunAl Hasan, Mohammad2023-10-272023-10-272022-06-28Zhuang, J., & Hasan, M. A. (2022). Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-Supervision. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), Article 4. https://doi.org/10.1609/aaai.v36i4.20362https://hdl.handle.net/1805/36745In 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.en-USPublisher PolicyData Mining & Knowledge Management (DMKM)Machine Learning (ML)Conference proceedings