A Structural Evolution-Based Anomaly Detection Method for Generalized Evolving Social Networks. (30th December 2020) - Wang, Huan; Gao, Qing; Li, Hao; Wang, Hao; Yan, Liping; Liu, Guanghua
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.