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

所属专题： Artificial Intelligence and Pattern Recognition； Data Management and Data Mining

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇

基于联合聚类的微博异常检测

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
  • 收稿日期:2014-11-17 修回日期:2015-07-12 出版日期:2015-09-05 发布日期:2015-09-05
  • 通讯作者: Guo-Wei Shen E-mail:shenguowei@hrbeu.edu.cn
  • 作者简介: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.
  • 基金资助:

    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.

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.

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传统的微博异常检测算法将用户和消息分开检测, 而随着异常用户的智能性越来越高, 检测效果显著下降。本文提出了一个新的基于二部图的联合聚类框架同时检测异常用户和消息。在该框架中, 将用户和消息之间的异质交互、同质交互采用二部图建模, 并通过非负矩阵三分解实现异常用户和消息同时检测。同质交互关系作为约束条件融合到联合聚类算法中, 进而提高联合聚类算法的准确率。在新浪微博数据集上的实验表明, 本文提出的算法在检测异常用户和消息时具有较高的准确率, 并且能够处理个体异常和群体异常。

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 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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