CDPM: Finding and Evaluating Community Structure in Social Networks

被引：0

作者：

Wan, Li ^{[1
]}

Liao, Jianxin ^{[1
]}

Zhu, Xiaomin ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China

来源：

ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS | 2008年 / 5139卷

关键词：

community detection; percolation; clique;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we proposed a CDPM (Clique Directed Percolation Method) algorithm, which clusters tightly cohesive cliques as cluster atoms and merge the cluster atoms into communities under the direction of a proposed object function, namely Structure Silhouette Coefficient (SSC). SSC could measure the quality of community divisions which allows communities share actors. Experiments demonstrate our algorithm can divide social networks into communities at a higher quality than compared algorithms.

引用

页码：620 / 627

页数：8

共 14 条

[11] Uncovering the overlapping community structure of complex networks in nature and society [J].