Enhancing Robustness of Graph Neural Networks on Social Media with Explainable Inverse Reinforcement Learning

Adversarial attacks against graph neural networks (GNNs) through perturbations of the graph structure are increasingly common in social network tasks like rumor detection. Social media platforms capture diverse attack sequence samples through both machine and manual screening processes. Investigating effective ways to leverage these adversarial samples to enhance robustness is imperative. We improve the maximum entropy inverse reinforcement learning (IRL) method with the mixtureof-experts approach to address multi-source graph adversarial attacks.