Abstract:

In this work, we propose a hybrid approach to community and anomaly detection in social networks, focusing on the combined challenges of analyzing network structures and identifying irregular behaviors. Our method integrates modularitybased community detection with structural and attribute-based similarity measures for anomaly detection. To validate its effectiveness, we evaluate the approach on both synthetic and real-world datasets, where it demonstrates robust performance, achieving superior precision, recall, and F1 scores compared to state-of-the-art techniques. This framework not only provides a scalable and efficient solution for analyzing complex networks but also enhances their security by uncovering critical irregularities.