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cs.LG2024
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework
Jinyu Cai, Yunhe Zhang, Zhoumin Lu +2
Graph anomaly detection (GAD) aims to identify anomalous graphs that significantly deviate from other ones, which has raised growing attention due to the broad existence and comple…
cs.LG2023
Self-Discriminative Modeling for Anomalous Graph Detection
Jinyu Cai, Yunhe Zhang, Jicong Fan
This paper studies the problem of detecting anomalous graphs using a machine learning model trained on only normal graphs, which has many applications in molecule, biology, and soc…
cs.LG2023
Deep Orthogonal Hypersphere Compression for Anomaly Detection
Yunhe Zhang, Yan Sun, Jinyu Cai +1
Many well-known and effective anomaly detection methods assume that a reasonable decision boundary has a hypersphere shape, which however is difficult to obtain in practice and is…