社会网络的数据规模在不断扩大,现存的异常检测算法对复杂社会网络进行检测的效果不理想,提出了一种基于图模块度聚类的异常检测算法(anomaly detection algorithm based on graph modularity clustering,GMC_AD),该算法适用于解决受网...社会网络的数据规模在不断扩大,现存的异常检测算法对复杂社会网络进行检测的效果不理想,提出了一种基于图模块度聚类的异常检测算法(anomaly detection algorithm based on graph modularity clustering,GMC_AD),该算法适用于解决受网络规模以及复杂度的限制导致检测效率不高的问题。GMC_AD算法在分析网络拓扑结构的基础上,通过引入异常节点加权机制和模块度聚类算法进行异常检测。GMC_AD算法主要在三个方面进行改进:a)设计网络中节点演化的量化策略,以此识别具有异常演化行为的节点来得到异常节点集合;b)通过模块度聚类的方法降低网络规模;c)在计算网络波动值的过程中使用加权机制合理考虑异常节点的影响,再通过网络波动值变化来检测异常。基于真实社会网络VAST、EU_E-mail和ENRON进行对比实验,GMC_AD算法准确地检测出异常发生的时段,实验结果显示在事件检测敏感性上提高了50%~82%,在异常检测运行效率上提高了30%~70%。实验结果表明,GMC_AD算法不仅提高了异常检测算法的准确率和敏感性,还提高了异常检测算法的效率。展开更多
This paper focuses on some key problems in web community discovery and link analysis.Based on the topic-oriented technology,the characteristics of a bipartite graph are studied.An Х bipartite core set is introduced t...This paper focuses on some key problems in web community discovery and link analysis.Based on the topic-oriented technology,the characteristics of a bipartite graph are studied.An Х bipartite core set is introduced to more clearly define extracting ways.By scanning the topic subgraph to construct Х bipartite graph and then prune the graph with i and j ,an Х bipartite core set,which is also the minimum element of a community,can be found.Finally,a hierarchical clustering algorithm is applied to many Х bipartite core sets and the dendrogram of the community inner construction is obtained.The correctness of the constructing and pruning method is proved and the algorithm is designed.The typical datasets in the experiment are prepared according to the way in HITS（hyperlink-induced topic search）.Ten topics and four search engines are chosen and the returned results are integrated.The modularity,which is a measure of the strength of the community structure in the social network,is used to validate the efficiency of the proposed method.The experimental results show that the proposed algorithm is effective and efficient.展开更多
文摘社会网络的数据规模在不断扩大,现存的异常检测算法对复杂社会网络进行检测的效果不理想,提出了一种基于图模块度聚类的异常检测算法(anomaly detection algorithm based on graph modularity clustering,GMC_AD),该算法适用于解决受网络规模以及复杂度的限制导致检测效率不高的问题。GMC_AD算法在分析网络拓扑结构的基础上,通过引入异常节点加权机制和模块度聚类算法进行异常检测。GMC_AD算法主要在三个方面进行改进:a)设计网络中节点演化的量化策略,以此识别具有异常演化行为的节点来得到异常节点集合;b)通过模块度聚类的方法降低网络规模;c)在计算网络波动值的过程中使用加权机制合理考虑异常节点的影响,再通过网络波动值变化来检测异常。基于真实社会网络VAST、EU_E-mail和ENRON进行对比实验,GMC_AD算法准确地检测出异常发生的时段,实验结果显示在事件检测敏感性上提高了50%~82%,在异常检测运行效率上提高了30%~70%。实验结果表明,GMC_AD算法不仅提高了异常检测算法的准确率和敏感性,还提高了异常检测算法的效率。
基金The National Natural Science Foundation of China(No.60773216)the National High Technology Research and Development Program of China(863Program)(No.2006AA010109)+1 种基金the Natural Science Foundation of Renmin University of China(No.06XNB052)Free Exploration Project(985 Project of Renmin University of China)(No.21361231)
文摘This paper focuses on some key problems in web community discovery and link analysis.Based on the topic-oriented technology,the characteristics of a bipartite graph are studied.An Х bipartite core set is introduced to more clearly define extracting ways.By scanning the topic subgraph to construct Х bipartite graph and then prune the graph with i and j ,an Х bipartite core set,which is also the minimum element of a community,can be found.Finally,a hierarchical clustering algorithm is applied to many Х bipartite core sets and the dendrogram of the community inner construction is obtained.The correctness of the constructing and pruning method is proved and the algorithm is designed.The typical datasets in the experiment are prepared according to the way in HITS（hyperlink-induced topic search）.Ten topics and four search engines are chosen and the returned results are integrated.The modularity,which is a measure of the strength of the community structure in the social network,is used to validate the efficiency of the proposed method.The experimental results show that the proposed algorithm is effective and efficient.
