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

Author:

Wang Huan1,Gao Qing2,Li Hao1,Wang Hao3,Yan Liping4,Liu Guanghua5

Affiliation:

1. College of Informatics, Huazhong Agricultural University, No. 1, Shizishan Street, Hongshan District, Wuhan, Hubei Province 430070, China

2. Department of Ophthalmology and Physiology Weill Institute for Neurosciences, University of California, San Francisco, 1701 Divisadero St, San Francisco, CA 94115, USA

3. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, No. 2, chongwen Road, Nanan District, Chongqing 400065, China

4. School of Software, East China Jiaotong University, Jiangxi Province, 808 shuanggang Street, Nanchang, Jiangxi Province 330052, China

5. School of Physics, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan 430074, China

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.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Nature Science Foundation of Hubei Province

Chongqing Natural Science Foundation Project

Independent Science and technology Innovation Fund project of Huazhong Agricultural University

Publisher

Oxford University Press (OUP)

Subject

General Computer Science

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