A Structural Evolution-Based Anomaly Detection Method for Generalized Evolving Social Networks

• Published in: Computer journal Vol. 65; no. 5; pp. 1189 - 1199
• Main Authors: Wang, Huan, Gao, Qing, Li, Hao, Wang, Hao, Yan, Liping, Liu, Guanghua
• Format: Journal Article
• Language: English
• Published: Oxford University Press 17.05.2022
• Subjects: evolving social network; anomaly detection; evolutional state; macroscopic evolution
• ISSN: 0010-4620; 1460-2067
• DOI: 10.1093/comjnl/bxaa168

Abstract Recently, text-based anomaly detection methods have obtained impressive results in social network services, but their applications are limited to social texts provided by users. To propose a method for generalized evolving social networks that have limited structural information, this study proposes a novel structural evolution-based anomaly detection method ($SeaDM$), which mainly consists of an evolutional state construction algorithm ($ESCA$) and an optimized evolutional observation algorithm ($OEOA$). $ESCA$ characterizes the structural evolution of the evolving social network and constructs the evolutional state to represent the macroscopic evolution of the evolving social network. Subsequently, $OEOA$ reconstructs the quantum-inspired genetic algorithm to discover the optimized observation vector of the evolutional state, which maximally reflects the state change of the evolving social network. Finally, $SeaDM$ combines $ESCA$ and $OEOA$ to evaluate the state change degrees and detect anomalous changes to report anomalies. Experimental results on real-world evolving social networks with artificial and real anomalies show that our proposed $SeaDM$ outperforms the state-of-the-art anomaly detection methods.