Open AccessMulti-resolution density modularity for finding community structure in complex networks
Author(s) -
Cong Zhang,
Shen Hui-Zhang,
Feng Liu,
Yang He-Qun
Publication year - 2012
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.61.148902
Subject(s) - modularity (biology) , community structure , computer science , resolution (logic) , complex network , clique percolation method , function (biology) , limit (mathematics) , complex system , artificial intelligence , data mining , mathematics , statistics , evolutionary biology , world wide web , biology , mathematical analysis , genetics
In reality many complex networks present modules or community structures obviously. Modularity is a benefit function used in quantifying the quality of a division of a network into communities. And it usually can be used as a basis for optimization methods of detecting community structure in networks. But the most popular modularity which is proposed by M. E. J. Newman and M. Girvan has the resolution limit in community detection. Multi-resolution modularity cannot overcome the misclassifications caused by merging and splitting the communities either. In this paper, we propose a multi-resolution density modularity based on the network density. The proposed function is tested on the artificial networks. Computational results show that it can reduce the rate of misclassification considerably. And the systematicness of the community structures can be demonstrated by the multi-resolution density modularity.