计算机与数字工程2013，Vol.41Issue(3)：388-390,456,4.
动态复杂网络社区挖掘—选择性聚类融合算法
Community Mining In Dynamic Complex Network:Selective Clustering Fusion Algorithm
摘要
Abstract
Research on dynamic complex network mining is a hot topic currently. Based on selective clustering fusion, this paper proposes a dynamic complex network mining algorithm. Firstly, the algorithm divides the dynamic process into snapshots with same time interval. According to the conceptions such as similarity and centrality of vertices, every snapshot gets the corresponding clustering outcomes in a speed accelerated by an improved hierarchical clustering algorithm. Secondly, the clustering results collection have to be selected on the basis of difference between clustering outcomes with aim to get various clustering members required in the following fusion process. Lastly, this paper comes up with the conception of weighted Co-association matrix in terms of time attenuation, and then obtains the final clustering results using the single-link algorithm. The clustering accuracy and the degree of dynamic characteristic mining are tested in the stochastic network and the real network. Experimental results demonstrate the feasibility and validity of this algorithm.关键词
动态复杂网络/社区挖掘/选择性聚类融合/加权共联矩阵/层次聚类Key words
dynamic complex network/selective clustering fusion/weighted Co-association matrix分类
信息技术与安全科学引用本文复制引用
张震,梁永全,张行林..动态复杂网络社区挖掘—选择性聚类融合算法[J].计算机与数字工程,2013,41(3):388-390,456,4.基金项目
国家自然科学基金专项基金项目(编号:71240003) （编号:71240003）
山东省自然科学基金项目(编号:ZR2012FM003)资助. （编号:ZR2012FM003）
