Anomaly Detection in Microblogging via Co-Clustering  

作　　者：杨武 申国伟 王巍 宫良一 于淼 董国忠 

机构地区：[1]Information Security Research Center, Harbin Engineering University, Harbin 150001, China

出　　处：《Journal of Computer Science & Technology》2015年第5期1097-1108,共12页计算机科学技术学报（英文版）

基　　金：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 Fhnds for the Central Universities of China under Grant No. HEUCF100605.

摘　　要：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.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.

关 键 词：MICROBLOGGING anomaly detection nonnegative matrix tri-factorization user interaction behavior 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术] TP391.41[自动化与计算机技术—计算机科学与技术]