Mitigating Emergent Robustness Degradation while Scaling Graph Learning

While graph neural networks have exhibited remarkable performance in various graph tasks, a significant concern is their vulnerability to adversarial attacks. Consequently, many defense methods have been proposed to alleviate the deleterious effects of adversarial attacks and learn robust graph representations. However, most of them are difficult to simultaneously avoid two major limitations: 1) an emergent and severe degradation in robustness when exposed to very intense attacks, and 2) heavy computation complexity hinders them from scaling to large graphs. In response to these challenges, we introduce an innovative graph defense method for unpredictable real-world scenarios by designing a graph robust learning framework that is resistant to robustness degradation and refraining from the unscalable designs with heavy computation: specifically, our method employs a denoising module, which eliminates edges that are associated with attacked nodes to reconstruct a cleaner graph; Then, it applies Mixture-of-Experts to select differentially private noises with varying magnitudes to counteract the hidden features attacked at different intensities toward robust predictions; Moreover, our overall design avoids the reliance on heavy adjacency matrix computations, such as SVD, thus facilitating its applicability even on large graphs. Comprehensive experiments have been conducted to demonstrate the anti-degraded robustness and scalability of our method, as compared to popular graph adversarial learning methods, under diverse attack intensities and various datasets of different sizes.

• Xiangchi is mentored by Chunhui; both contributed equally and are listed alphabetically by last name.

Published as a conference paper at ICLR 2024 GNNGuard (Zhang & Zitnik, 2020), which require dense adjacency matrix computations. This can result in substantial computational overhead, as shown in the experiment section, leading to out-of-memory problems when applied to larger datasets like Flick, Reddit, and AMiner, especially when using a 32 GB GPU. These challenges necessitate innovative solutions to enhance the robustness and scalability of GNNs against adversarial attacks. 0 100 200 300 400 500 600 700