MoEGAD: A Mixture-of-Experts Framework With Pseudo-Anomaly Generation for Graph-Level Anomaly Detection.

Abstract

Graph-level anomaly detection (GLAD) aims to identify graphs that significantly deviate from the norm. Despite remarkable advancements in recent years, existing GLAD approaches struggle with the scarcity of labeled anomalies. Although some semi-supervised approaches leverage a small fraction of anomalous graphs during training, the limited diversity of these anomalies poses challenges in learning robust decision boundaries. Additionally, the detection of multi-task graph anomalies, a prevalent challenge in real-world scenarios, remains largely unexplored. To bridge these gaps, we propose MoEGAD, a novel framework leveraging a mixture of experts (MoE) architecture for GLAD. MoEGAD introduces an iterative anomalous graph generation module to produce pseudo-anomalous graphs, which facilitates the subsequent decision boundary learning. An early stopping mechanism is incorporated to ensure that the generated anomalies preserve sufficient dissimilarity from normal graphs. More importantly, we also propose a latent MoE module comprising multiple expert networks alongside a specialized gating network, which promotes cross-task adaptability for diverse GLAD problems. To the best of our knowledge, this is the first work exploring the potential of MoE architecture in the context of GLAD. Extensive experiments across single-task, large-scale, and multi-task scenarios demonstrate that MoEGAD significantly outperforms state-of-the-art GLAD baselines.