DGAM: Dual-Guided Anomaly Mining for Semi-Supervised Graph Anomaly Detection

For the challenging scenario in which only normal node labels are available in semi-supervised graph anomaly detection, existing generative methods usually synthesize abnormal nodes through random perturbation or feature interpolation. However, these methods fail to consider node abnormality compreh...

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Bibliografiske detaljer

Principais autores:	Xingxuan Li, Ting Guo, Zhen Tian
Format:	Artigo
Sprog:	Inglês
Udgivet:	MDPI AG 2026-05-01
Serier:	Information
Fag:	graph neural network graph anomaly detection semi-supervised graph anomaly detection
Online adgang:	https://www.mdpi.com/2078-2489/17/6/521
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