Comparing local modularity optimization for detecting communities in networks

被引：7

作者：

Xiang, Ju ^{[1
]}

Wang, Zhi-Zhong ^{[2
]}

Li, Hui-Jia ^{[3
,4
]}

Zhang, Yan ^{[5
]}

Chen, Shi ^{[5
]}

Liu, Cui-Cui ^{[5
]}

Li, Jian-Ming ^{[1
]}

Guo, Li-Juan ^{[6
]}

机构：

[1] Changsha Med Univ, Neurosci Res Ctr, Changsha 410219, Hunan, Peoples R China

[2] Hunan First Normal Univ, South City Coll, Changsha 410205, Hunan, Peoples R China

[3] Cent Univ Finance & Econ, Sch Management Sci & Engn, Beijing 100080, Peoples R China

[4] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China

[5] Changsha Med Univ, Dept Comp Sci, Changsha 410219, Hunan, Peoples R China

[6] Changsha Med Univ, Dept Basic Med Sci, Changsha 410219, Hunan, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MODERN PHYSICS C | 2017年 / 28卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Community structure; community detection; complex networks; COMPLEX NETWORKS;

D O I：

10.1142/S012918311750084X

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Community detection is one important problem in network theory, and many methods have been proposed for detecting community structures in the networks. Given quality functions for evaluating community structures, community detection can be considered as one kind of optimization problem, such as modularity optimization, therefore, optimization of quality functions has been one of the most popular strategies for community detection. In this paper, we introduced two kinds of local modularity functions for community detection, and the self consistent method is introduced to optimize the local modularity for detecting communities in the networks. We analyze the behaviors of the modularity optimizations, and compare the performance of them in community detection. The results confirm the superiority of the local modularity in detecting community structures, especially on large-size and heterogeneous networks.

引用

页数：11

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