Robust Tensor Graph Convolutional Networks via T-SVD based Graph Augmentation

Abstract

Graph Neural Networks (GNNs) have exhibited their powerful ability of tackling nontrivial problems on graphs. However, as an extension of deep learning models to graphs, GNNs are vulnerable to noise or adversarial attacks due to the underlying perturbations propagating in message passing scheme, which can affect the ultimate performances dramatically. Thus, it's vital to study a robust GNN framework to defend against various perturbations. In this paper, we propose a Robust Tensor Graph Convolutional Network (RT-GCN) model to improve the robustness. On the one hand, we utilize multi-view augmentation to reduce the augmentation variance and organize them as a third-order tensor, followed by the truncated T-SVD to capture the low-rankness of the multi-view augmented graph, which improves the robustness from the perspective of graph preprocessing. On the other hand, to effectively capture the inter-view and intra-view information on the multi-view augmented graph, we propose tensor GCN (TGCN) framework and analyze the mathematical relationship between TGCN and vanilla GCN, which improves the robustness from the perspective of model architecture. Extensive experimental results have verified the effectiveness of RT-GCN on various datasets, demonstrating the superiority to the state-of-the-art models on diverse adversarial attacks for graphs.