Towards Self-Interpretable Graph-Level Anomaly Detection

no code implementations • NeurIPS 2023 • Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan

In this paper, we investigate a new challenging problem, explainable GLAD, where the learning objective is to predict the abnormality of each graph sample with corresponding explanations, i. e., the vital subgraph that leads to the predictions.

Graph Anomaly Detection