Adversarial Robustness in Graph Neural Networks: Recent Advances and New Frontier

Zhichao Hou, Minhua Lin, MohamadAli Torkamani, Suhang Wang, Xiaorui Liu

Abstract: In recent years, Graph Neural Networks (GNNs) have attracted substantial attention due to their powerful ability in modeling graph-structured data and broad applications across various domains such as social media, biology, health and finance. Despite these successes, GNNs exhibit significant vulnerabilities to adversarial attacks, which poses challenges to their reliable deployment in real scenarios. In this tutorial, we will provide an in-depth exploration of existing adversarial attacks and the state-of-the-art techniques in enhancing the robustness of GNNs. Participants will gain insights into advancing attack and defense methods, along with evaluation and comparison of robust GNN models. Besides a thorough overview on current landscape, we will also cover the summary and discussion on potential future directions, aiming to inspire more researchers to engage and innovate in this field.