A Structural Evolution-Based Anomaly Detection Method for Generalized Evolving Social Networks. (30th December 2020)

• Record Type: Journal Article
• Authors: Wang, Huan; Gao, Qing; Li, Hao; Wang, Hao; Yan, Liping; Liu, Guanghua
• Is Part Of: Computer journal. Volume 65:Number 5(2022)
• Page Start: 1189
• Page End: 1199
• Publication Date: 2020-12-30
• Subjects: anomaly detection -- evolving social network -- macroscopic evolution -- evolutional state; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxaa168
• Languages: English
• ISSNs: 0010-4620

Abstract

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.