GraphDefense: Towards Robust Graph Convolutional Networks
This addresses security-critical applications by improving GCN robustness against adversarial attacks, though it is incremental as it builds on existing adversarial training methods.
The paper tackles the vulnerability of graph convolutional networks (GCNs) to adversarial perturbations on graph data, proposing GraphDefense to defend against such attacks and showing that it successfully increases robustness while maintaining semi-supervised learning settings.
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.