Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks

Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks

In the burgeoning field of anomaly detection within attributed networks, traditional methodologies often encounter the intricacies of network complexity, particularly in capturing nonlinearity and sparsity. This study introduces an innovative approach that synergizes the strengths of graph convoluti...

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Bibliographic Details
Main Authors: Wasim Khan, Afsaruddin Mohd, Mohammad Suaib, Mohammad Ishrat, Anwar Ahamed Shaikh, Syed Mohd Faisal
Format: Article
Language:English
Published: KeAi Communications Co. Ltd. 2025-06-01
Series:Data Science and Management
Subjects:
Anomaly detection
Deep learning
Hypersphere learning
Residual modeling
Graph convolution network
Attention mechanism
Online Access:http://www.sciencedirect.com/science/article/pii/S2666764924000481
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http://www.sciencedirect.com/science/article/pii/S2666764924000481

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