Detecting the optimal number of communities in complex networks

被引：8

作者：

Li, Zhifang ^{[2
,3
]}

Hu, Yanqing ^{[1
,2
,3
]}

Xu, Beishan ^{[2
,3
]}

Di, Zengru ^{[2
,3
]}

Fan, Ying ^{[2
,3
]}

机构：

[1] SW Jiaotong Univ, Sch Math, Chengdu 610031, Peoples R China

[2] Beijing Normal Univ, Dept Syst Sci, Sch Management, Beijing 100875, Peoples R China

[3] Beijing Normal Univ, Ctr Complex Res, Beijing 100875, Peoples R China

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2012年 / 391卷 / 04期

关键词：

Complex network; Community structure; Optimal community number; MODULARITY;

D O I：

10.1016/j.physa.2011.06.023

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

To obtain the optimal number of communities is an important problem in detecting community structures. In this paper, we use the extended measurement of community detecting algorithms to find the optimal community number. Based on the normalized mutual information index, which has been used as a measure for similarity of communities, a statistic Omega(c) is proposed to detect the optimal number of communities. In general, when Omega(c) reaches its local maximum, especially the first one, the corresponding number of communities c is likely to be optimal in community detection. Moreover, the statistic Omega(c) can also measure the significance of community structures in complex networks, which has been paid more attention recently. Numerical and empirical results show that the index Omega(c) is effective in both artificial and real world networks. (C) 2011 Published by Elsevier B.V.

引用

页码：1770 / 1776

页数：7

共 37 条

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