Open AccessGlobal vs local modularity for network community detection
Author(s) -
Shi Chen,
Zhizhong Wang,
Liang Tang,
Yanni Tang,
Yuanyuan Gao,
Hui-Jia Li,
Xu Ju,
Yan Zhang
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0205284
Subject(s) - modularity (biology) , computer science , community structure , clique percolation method , complex network , set (abstract data type) , limit (mathematics) , function (biology) , complex system , network analysis , data mining , theoretical computer science , artificial intelligence , distributed computing , mathematics , world wide web , mathematical analysis , genetics , physics , combinatorics , quantum mechanics , evolutionary biology , biology , programming language
Community structures are ubiquitous in various complex networks, implying that the networks commonly be composed of groups of nodes with more internal links and less external links. As an important topic in network theory, community detection is of importance for understanding the structure and function of the networks. Optimizing statistical measures for community structures is one of most popular strategies for community detection in complex networks. In the paper, by using a type of self-loop rescaling strategy, we introduced a set of global modularity functions and a set of local modularity functions for community detection in networks, which are optimized by a kind of the self-consistent method. We carefully compared and analyzed the behaviors of the modularity-based methods in community detection, and confirmed the superiority of the local modularity for detecting community structures on large-size and heterogeneous networks. The local modularity can more quickly eliminate the first-type limit of modularity, and can eliminate or alleviate the second-type limit of modularity in networks, because of the use of the local information in networks. Moreover, we tested the methods in real networks. Finally, we expect the research can provide useful insight into the problem of community detection in complex networks.