基于边聚类的多层社会网络社团发现算法  

Community discovery in multi-layered social network based on edge clustering

作　　者：袁辉辉[1] 曹玉林[1,2] 王小明[1] 

机构地区：[1]陕西师范大学计算机科学学院,西安710062 [2]青海师范大学计算机学院,西宁810008

出　　处：《计算机应用研究》2014年第2期351-353,377,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(61173094);国家教育部留学回国人员科研启动基金资助项目

摘　　要：针对目前多层社会网络(multi-layered social network,MSN)的社团发现算法较少、社团划分结果较粗糙等特点,提出了一种基于边聚类的多层社会网络社团发现(CLEDCC)算法。该算法综合考虑每层关系网中的任意两节点邻居及节点本身的关系强弱,并分别针对人造稀疏网、稠密网以及真实数据集进行仿真。实验表明,所提出的CLEDCC算法能有效地避免参数不确定性问题,并比跨层边聚类系数(CLECC)算法的社团划分结果更精准。For that the existing community discovery algorithms of muhi-layered social network （MSN） are lack and their re- suits are crude, this paper presented cross-layer edge differential clustering coefficient（CLEDCC） algorithm in MSN based on edge clustering. This algorithm considered the strength of the relationships about any two nodes and their neighbors in each layer network, and respectively simulated for artificial sparse network, dense network and real datasets. Experimental results show that CLEDCC algorithm can effectively avoid the problem of parameter uncertainty, which is more accurate than the cross- layer edge clustering coefficient（CLECC） algorithm in community discovery.

关 键 词：社会网络 多层社会网络 社团发现 跨层边差分聚类系数 模块度 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] TP301.6[自动化与计算机技术—计算机科学与技术]