GraphDefense: Towards Robust Graph Convolutional Networks

TL;DR

This paper introduces GraphDefense, a novel adversarial training method that enhances the robustness of Graph Convolutional Networks against perturbations, maintaining semi-supervised learning and scalability to large graphs.

Contribution

Proposes GraphDefense, an adversarial training approach that improves GCN robustness while preserving semi-supervised learning and scalability.

Findings

GraphDefense significantly increases GCN robustness to adversarial attacks.

Adversarial training on features is equivalent to training on edges with small perturbations.

The method scales effectively to large datasets like Reddit.

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.