[2601.19102] OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection

Computer Science > Machine Learning arXiv:2601.19102 (cs) [Submitted on 27 Jan 2026 (v1), last revised 26 Mar 2026 (this version, v2)] Title:OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection Authors:Lecheng Zheng, Dongqi Fu, Zihao Li, Jingrui He View a PDF of the paper titled OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection, by Lecheng Zheng and 3 other authors View PDF HTML (experimental) Abstract:Graph data is informative to represent complex relationships such as transactions between accounts, communications between devices, and dependencies among machines or processes. Correspondingly, graph anomaly detection (GAD) plays a critical role in identifying anomalies across various domains, including finance, cybersecurity, manufacturing, etc. Facing the large-volume and multi-domain graph data, nascent efforts attempt to develop foundational generalist models capable of detecting anomalies in unseen graphs without retraining. To the best of our knowledge, the different feature semantics and dimensions of cross-domain graph data heavily hinder the development of the graph foundation model, leaving further in-depth continual learning and inference capabilities a quite open problem. Hence, we propose OWLEYE, a novel zero-shot GAD framework that learns transferable patterns of normal behavior from multiple graphs, with a threefold contribution. First, OWLEYE proposes a cross-domain feature alignment module to harmonize feature distri...