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cs.LG2025
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective
Di Jin, Jingyi Cao, Xiaobao Wang +4
Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection. Existing methods…
cs.LG2023
Ahpatron: A New Budgeted Online Kernel Learning Machine with Tighter Mistake Bound
Yun Liao, Junfan Li, Shizhong Liao +2
In this paper, we study the mistake bound of online kernel learning on a budget. We propose a new budgeted online kernel learning model, called Ahpatron, which significantly improv…