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Inductive Anomaly Detection on Attributed Networks
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Anomaly detection on attributed networks has attracted a surge of research attention due to its broad applications in various high-impact domains, such as security, finance, and healthcare. Nonetheless, most of the existing efforts do not naturally generalize to unseen nodes, leading to the fact that people have to retrain the detection model from scratch when dealing with newly observed data. In this study, we propose to tackle the problem of inductive anomaly detection on attributed networks
doi:10.24963/ijcai.2020/179
dblp:conf/ijcai/DingLAL20
fatcat:ayeg35tdazgydkpkot44k5qwse