Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks

Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

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Hongwei Jin, Zhan Shi, Venkata Jaya Shankar Ashish Peruri, Xinhua Zhang


Graph convolution networks (GCNs) have become effective models for graph classification. Similar to many deep networks, GCNs are vulnerable to adversarial attacks on graph topology and node attributes. Recently, a number of effective attack and defense algorithms have been designed, but no certificate of robustness has been developed for GCN-based graph classification under topological perturbations with both local and global budgets. In this paper, we propose the first certificate for this problem. Our method is based on Lagrange dualization and convex envelope, which result in tight approximation bounds that are efficiently computable by dynamic programming. When used in conjunction with robust training, it allows an increased number of graphs to be certified as robust.