Revisiting Adversarial Robustness of GNNs Against Structural Attacks: A Simple and Fast Approach

To defend against adversarial structural attacks on graphs, we analyze attacks through the lens of mutual information and discover the “pairwise effect". This effect reveals that structural attacks effectively degrade the performance of victim GNNs when these GNNs receive the modified structure paired with the given node attributes as training input. Therefore, we propose a novel defense strategy that renders structural attacks ineffective by disrupting the pairing of modified structures and node attributes during the training of victim GNNs, which we call “disrupting the pairwise effect". To implement this idea, we propose two simple yet effective training strategies: Structural Fine-Tuning (SF) and Progressive Structural Training (PST), which disrupt the pairwise effect through node attributes pre-training followed by structure fine-tuning and progressive structure training, respectively. Compared to existing robust GNNs, our strategies avoid time-consuming techniques, thereby improving the robustness of GNNs while enhancing training speed. Additionally, these strategies can be easily applied to a wide range of commonly used GNNs, including robust GNN variants, making them highly adaptable to different models and applications. We provide theoretical analysis of the proposed training strategies and conduct extensive experiments on various datasets to demonstrate their effectiveness. Datasets and codes of this paper are available at https://github.com/Xing-Ai1003/Revisiting-Adversarial-Robustness-of-GNNs.