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Anomaly Detection in Microblogging via Co-Clustering

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Abstract

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 co-clustering 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 co-clustering 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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Authors and Affiliations

  1. Information Security Research Center, Harbin Engineering University, Harbin, 150001, China

    Wu Yang, Guo-Wei Shen, Wei Wang, Liang-Yi Gong, Miao Yu & Guo-Zhong Dong

Authors
  1. Wu Yang
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  2. Guo-Wei Shen
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  3. Wei Wang
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  4. Liang-Yi Gong
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  5. Miao Yu
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  6. Guo-Zhong Dong
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Corresponding author

Correspondence to Guo-Wei Shen.

Additional information

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.

A preliminary version of the paper was published in the Proceedings of SMP 2014.

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Yang, W., Shen, GW., Wang, W. et al. Anomaly Detection in Microblogging via Co-Clustering. J. Comput. Sci. Technol. 30, 1097–1108 (2015). https://doi.org/10.1007/s11390-015-1585-3

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  • DOI: https://doi.org/10.1007/s11390-015-1585-3

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