Global interpretable graph-level anomaly detection via prototype

Graph-level anomaly detection (GLAD) identifies graphs exhibiting abnormal properties within a graph dataset. Despite promising results in this task, the state-of-the-art methods cannot be fully trusted and deployed in realistic scenarios due to their black-box nature. To alleviate this, existing methods try to explain predictions by extracting important subgraphs from each graph, as instance-level explanations. However, instance-level explanations across all samples are costly to verify and insufficient to capture the model's general behaviors. Thus, we propose a global interpretable Graph-Level Anomaly Detection model via Prototype (GLADPro), which provides global-level explanations throughout the entire dataset, that is, the significant subgraph patterns that consistently influence the model's decisions. Specifically, GLADPro incorporates prototype learning with the information bottleneck principle, enabling prototypes to capture the most significant subgraph patterns as global-level explanations through persistent interactions with key subgraphs from input graphs. In addition, a regularization term is proposed to prevent the collapse traps with theoretical proof. Finally, we filter redundant prototypes using the maximum mean discrepancy metric. Extensive experiments demonstrate the superiority of GLADPro in anomaly detection and explainability; for instance, on the mutagen dataset, it reduces the number of explanations to verify from 1403 to only 6.