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...

وصف كامل

التفاصيل البيبلوغرافية

الحاوية / القاعدة:	Egyptian Informatics Journal
المؤلفون الرئيسيون:	Ismail Chetoui, Essaid El Bachari, Mohamed El Adnani, Mohamed Ouhssini
التنسيق:	مقال
اللغة:	الإنجليزية
منشور في:	Elsevier 2025-09-01
الموضوعات:	Graph convolutional networks Graph attention networks Graph autoencoders Anomaly detection Graph embeddings Education
الوصول للمادة أونلاين:	http://www.sciencedirect.com/science/article/pii/S1110866525001288