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Structural Evolution-Based Anomaly Detection Method for Generalized Evolving Social Networks.
- Source :
- Computer Journal; May2022, Vol. 65 Issue 5, p1189-1199, 11p
- Publication Year :
- 2022
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00104620
- Volume :
- 65
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Computer Journal
- Publication Type :
- Academic Journal
- Accession number :
- 156940117
- Full Text :
- https://doi.org/10.1093/comjnl/bxaa168