Anomaly detection in graph databases using graph neural networks: Identifying unusual patterns in graphs

Anomaly detection in graph-structured data is a critical task in various applications, including social networks, fraud detection, and educational platforms. This paper introduces a novel hybrid architecture that leverages Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Graph...

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Auteurs principaux:	Ismail Chetoui, Essaid El Bachari, Mohamed El Adnani, Mohamed Ouhssini
Format:	Artigo
Langue:	Inglês
Publié:	Elsevier 2025-09-01
Collection:	Egyptian Informatics Journal
Sujets:	Graph convolutional networks Graph attention networks Graph autoencoders Anomaly detection Graph embeddings Education
Accès en ligne:	http://www.sciencedirect.com/science/article/pii/S1110866525001288
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