Huimei Li

Unsupervised Graph-Level Anomaly Detection via Multi-granular Graph Structure Learning

Graph-level anomaly detection (GLAD) aims to identify graphs that deviate from the majority in a dataset of graphs. Existing methods typically adopt either a global aggregation perspective that summarizes nodes within a graph into a representation vector, or a subgraph-oriented perspective which reg