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

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.