Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization

Abstract

Graph Neural Networks (GNNs) have achieved state-of-the-art results on a variety of graph learning tasks, however, it has been demonstrated that they are vulnerable to adversarial attacks, raising serious security concerns. A lot of studies have been developed to train GNNs in a noisy environment and increase their robustness against adversarial attacks. However, existing methods have not uncovered a principled difficulty: the convoluted mixture distribution between clean and attacked data samples, which leads to sub-optimal model design and limits their frameworks’ robustness. In this work, we first begin by identifying the root cause of mixture distribution, then, for tackling it, we propose a novel method GAME - Graph Adversarial Mixture of Experts to enlarge the model capacity and enrich the representation diversity of adversarial samples, from three perspectives of model, training, and optimization. Specifically, we first propose a plug-and-play GAME layer that can be easily incorporated into any GNNs and enhance their adversarial learning capabilities. Second, we design a decoupling-based graph adversarial training in which the component of the model used to generate adversarial graphs is separated from the component used to update weights. Third, we introduce a graph diversity regularization that enables the model to learn diverse representation and further improves model performance. Extensive experiments demonstrate the effectiveness and advantages of GAME over the state-of-the-art adversarial training methods across various datasets given different attacks.

Zheyuan (Frank) Liu

Senior Student majoring in Computer Science and Applied Math

My research interests include Machine Learning, Cybersecurity, Deep Learning, Data-Efficient AI, Graph Mining