摘要
复杂网络社团发现的研究对于控制疾病传播、网络病毒的传播等具有重大意义。针对已有社团发现算法时间复杂度过高,不适用于结构未知的大型网络等问题,结合谱聚类在识别未知分布数据集聚类方面的优势,以及模块度函数能够在大型网络中搜寻出最佳社团数目的能力,提出了基于谱聚类的社团发现算法——SCCF算法。实验结果表明,与已有的社团发现算法相比,SCCF算法效率更高,并且能够在网络节点数上万的大型网络中得到高质量的社团结构。
Research on community finding is very helpful to control virus spreading in networks. Most of the proposed community-finding algorithms are not suitable for very large networks because of their time-complexity. Combined with the advantage of solving the clustering of unknown distributed data set of the spectral clustering,and the ability of modularity function in finding good community number in large networks, a community-finding algorithm based on spectral clustering was proposed. Experimental results indicate that the new algorithm is efficient and effective at finding good community structure in large networks.
出处
《计算机科学》
CSCD
北大核心
2009年第9期49-50,95,共3页
Computer Science
基金
国家863计划项目(2005AA147030)资助
关键词
复杂网络
社团结构
谱聚类
模块度
Complex networks,Community structure,Spectral clustering,Modularity
