When bipartite graph learning meets anomaly detection in attributed networks: Understand abnormalities from each attribute

Detecting anomalies in attributed networks has become a subject of interest in both academia and industry due to its wide spectrum of applications. Although most existing methods achieve desirable performance by the merit of various graph neural networks, the way they bundle node-affiliated multidimensional attributes into a whole for embedding calculation hinders their ability to model and analyze anomalies at the fine-grained feature level. To characterize anomalies from each feature dimension, we propose Eagle, a deep framework based on bipartitE grAph learninG for anomaLy dEtection. Specifically, we disentangle instances and attributes as two disjoint and independent node sets, then formulate the input attributed network as an intra-connected bipartite graph that involves two different relations: edges across two types of nodes described by attribute values, and links between nodes of the same type recorded in the network topology. By learning a self-supervised edge-level prediction task, named affinity inference, Eagle has good physical sense in explaining abnormal deviations from each attribute. Experiments corroborate the effectiveness of Eagle under transductive and inductive task settings. Moreover, case studies illustrate that Eagle is more user-friendly as it opens the door for humans to understand abnormalities from the perspective of different feature combinations.