Adaptive Reliable Defense Graph for Multi-Channel Robust GCN

Abstract

Graph Convolutional Networks (GCNs) have demonstrated remarkable success in various graph-related tasks. However, recent studies show that GCNs are vulnerable to adversarial attacks on graph structures. Therefore, how to defend against such attacks has become a popular research topic. The current common defense methods face two main limitations: (1) From the data perspective, it may lead to suboptimal results since the structural information is ignored when distinguishing the perturbed edges. (2) From the model perspective, the defenders rely on the low-pass filter of the GCN, which is vulnerable during message passing. To overcome these limitations, this paper analyzes the characteristics of perturbed edges, and based on this we propose a robust defense framework, REDE, to generate the adaptive Reliable Defense graph for multi-channel robust GCN. REDE first uses feature similarity and structure difference to discriminate perturbed edges and generates the defense graph by pruning them. Then REDE designs a multi-channel GCN, which can separately capture the information of different edges and high-order neighbors utilizing different frequency components. Leveraging this capability, the defense graph is adaptively updated at each layer, enhancing its reliability and improving prediction accuracy. Extensive experiments on four benchmark datasets demonstrate the enhanced performance and robustness of our proposed REDE over the state-of-the-art defense methods.