Leilei Jiang

Multi-dimensional Adaptive Mix-hop Contextual Learning Framework for Universal Graph Anomaly Detection

Graph Anomaly Detection (GAD) focuses on identifying instances that deviate from normal patterns in graph-structured data. Although substantial progress has been made in this field, current approaches are constrained by the "one-dataset-one-model" paradigm, exhibiting limited generalization across h