An adversarial variational graph autoencoder with contrastive learning for robust anomaly detection in large scale attributed networks

Abstract Anomaly detection in attributed networks is challenging because it combines complex structural relationships with high-dimensional node attributes. Traditional methods rarely solve both problems simultaneously, making them less effective. We present a new approach for anomaly detection that...

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Detalles Bibliográficos

Principais autores:	Wasim Khan, Nadhem Ebrahim, Mohammad Ishrat, Hasan Alkahtani, Theyazn H. H. Aldhyani
Formato:	Artigo
Idioma:	Inglês
Publicado:	SpringerOpen 2025-12-01
Series:	Journal of Big Data
Assuntos:	Adversarial training Anomaly detection Attributed networks Contrastive learning Deep learning Variational graph autoencoder
Acceso en liña:	https://doi.org/10.1186/s40537-025-01342-z
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