GraphDefense: Towards Robust Graph Convolutional Networks

Abstract

In this paper, we study the robustness of graph convolutional networks (GCNs). Despite the good performance of GCNs on graph semi-supervised learning tasks, previous works have shown that the original GCNs are very unstable to adversarial perturbations. In particular, we can observe a severe performance degradation by slightly changing the graph adjacency matrix or the features of a few nodes, making it unsuitable for security-critical applications. Inspired by the previous works on adversarial defense for deep neural networks, and especially adversarial training algorithm, we propose a method called GraphDefense to defend against the adversarial perturbations. In addition, for our defense method, we could still maintain semi-supervised learning settings, without a large label rate. We also show that adversarial training in features is equivalent to adversarial training for edges with a small perturbation. Our experiments show that the proposed defense methods successfully increase the robustness of Graph Convolutional Networks. Furthermore, we show that with careful design, our proposed algorithm can scale to large graphs, such as Reddit dataset.

References in corpus (15)

• Semi-Supervised Classification with Graph Convolutional Networks
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Cited by in corpus (7)

• Adversarial Attack and Defense on Graph Data: A Survey
• A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
• When Does Self-Supervision Help Graph Convolutional Networks?
• A Survey of Adversarial Learning on Graphs
• Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
• Learning Robust Representation through Graph Adversarial Contrastive Learning
• On Generalization of Graph Autoencoders with Adversarial Training