A multi-stage hybrid framework for anomaly detection in attributed graphs using attention-driven representation and community-aware scoring

Abstract Detecting anomalies in attributed graphs is a crucial yet challenging task due to the interplay between node attributes, graph structure, and contextual dependencies. We propose a novel multi-stage hybrid framework, NHADF, that integrates three core modules to enhance detection capability a...

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Bibliographic Details

Published in:	Discover Applied Sciences
Main Authors:	Wasim Khan, Khan Vajid Nabilal, Mohammad Ishrat, Kashif Asad, Faheem Ahmad, Meenal Suraj Wagh
Format:	Article
Language:	English
Published:	Springer 2025-10-01
Subjects:	Attributed networks Anomaly detection Graph Neural Networks (GNNs) Embedding smoothing Attention mechanism Community consistency calibration
Online Access:	https://doi.org/10.1007/s42452-025-07801-9