›› 2015, Vol. 30 ›› Issue (5): 1097-1108.doi: 10.1007/s11390-015-1585-3

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Social Media Processing • Previous Articles     Next Articles

Anomaly Detection in Microblogging via Co-Clustering

Wu Yang(杨武), Senior Member, CCF, Member, ACM Guo-Wei Shen*(申国伟), Student Member, CCF, Member, ACM, Wei Wang(王巍), Liang-Yi Gong(宫良一) Miao Yu(于淼), Guo-Zhong Dong(董国忠)   

  1. Information Security Research Center, Harbin Engineering University, Harbin 150001, China
  • Received:2014-11-17 Revised:2015-07-12 Online:2015-09-05 Published:2015-09-05
  • Contact: Guo-Wei Shen E-mail:shenguowei@hrbeu.edu.cn
  • About author:Wu Yang received his Ph.D. degree in computer system architecture from Harbin Institute of Technology, Harbin, in 2005. He is currently a professor and doctoral supervisor of Harbin Engineering University. His main research interests include data mining, information security and wireless sensor network. He is a senior member of CCF and a member of ACM.
  • Supported by:

    This work was supported by the National Natural Science Foundation of China under Grant No. 61170242, the National High Technology Research and Development 863 Program of China under Grant No. 2012AA012802, and the Fundamental Research Funds for the Central Universities of China under Grant No. HEUCF100605.

Traditional anomaly detection on microblogging mostly focuses on individual anomalous users or messages. Since anomalous users employ advanced intelligent means, the anomaly detection is greatly poor in performance. In this paper, we propose an innovative framework of anomaly detection based on bipartite graph and co-clustering. A bipartite graph between users and messages is built to model the homogeneous and heterogeneous interactions. The proposed coclustering algorithm based on nonnegative matrix tri-factorization can detect anomalous users and messages simultaneously. The homogeneous relations modeled by the bipartite graph are used as constraints to improve the accuracy of the coclustering algorithm. Experimental results show that the proposed scheme can detect individual and group anomalies with high accuracy on a Sina Weibo dataset.

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