AI RESEARCH
Mitigating Homophily Disparity in Graph Anomaly Detection: A Scalable and Adaptive Approach
arXiv CS.LG
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ArXi:2603.08137v1 Announce Type: new Graph anomaly detection (GAD) aims to identify nodes that deviate from normal patterns in structure or features. While recent GNN-based approaches have advanced this task, they struggle with two major challenges: 1) homophily disparity, where nodes exhibit varying homophily at both class and node levels; and 2) limited scalability, as many methods rely on costly whole-graph operations. To address them, we propose SAGAD, a Scalable and Adaptive framework for.