The effect of weight on community structures is investigated in this paper. We use weighted modularity Q w to evaluate the partitions and weighted extremal optimization algorithm to detect communities. Starting from empirical and idealized weighted networks, the matching between weights and edges are disturbed. Then using similarity function S to measure the difference between community structures, it is found that the redistribution of weights does strongly affect the community structure especially in dense networks. This indicates that the community structure in networks is a suitable property to reflect the role of weight.
%0 Journal Article
%1 Fan2007Effect
%A Fan, Ying
%A Li, Menghui
%A Zhang, Peng
%A Wu, Jinshan
%A Di, Zengru
%D 2007
%J Physica A: Statistical Mechanics and its Applications
%K bipartite-networks, communities weighted-networks
%N 2
%P 583--590
%R 10.1016/j.physa.2006.12.021
%T The effect of weight on community structure of networks
%U http://dx.doi.org/10.1016/j.physa.2006.12.021
%V 378
%X The effect of weight on community structures is investigated in this paper. We use weighted modularity Q w to evaluate the partitions and weighted extremal optimization algorithm to detect communities. Starting from empirical and idealized weighted networks, the matching between weights and edges are disturbed. Then using similarity function S to measure the difference between community structures, it is found that the redistribution of weights does strongly affect the community structure especially in dense networks. This indicates that the community structure in networks is a suitable property to reflect the role of weight.
@article{Fan2007Effect,
abstract = {{The effect of weight on community structures is investigated in this paper. We use weighted modularity Q w to evaluate the partitions and weighted extremal optimization algorithm to detect communities. Starting from empirical and idealized weighted networks, the matching between weights and edges are disturbed. Then using similarity function S to measure the difference between community structures, it is found that the redistribution of weights does strongly affect the community structure especially in dense networks. This indicates that the community structure in networks is a suitable property to reflect the role of weight.}},
added-at = {2019-06-10T14:53:09.000+0200},
author = {Fan, Ying and Li, Menghui and Zhang, Peng and Wu, Jinshan and Di, Zengru},
biburl = {https://www.bibsonomy.org/bibtex/24fd2917b5f7aff76658d49bc4b4daf64/nonancourt},
citeulike-article-id = {6964362},
citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.physa.2006.12.021},
day = 15,
doi = {10.1016/j.physa.2006.12.021},
interhash = {f9239eb41105d176584cea01295996d8},
intrahash = {4fd2917b5f7aff76658d49bc4b4daf64},
issn = {03784371},
journal = {Physica A: Statistical Mechanics and its Applications},
keywords = {bipartite-networks, communities weighted-networks},
month = may,
number = 2,
pages = {583--590},
posted-at = {2017-06-28 17:13:21},
priority = {2},
timestamp = {2019-08-20T16:57:00.000+0200},
title = {{The effect of weight on community structure of networks}},
url = {http://dx.doi.org/10.1016/j.physa.2006.12.021},
volume = 378,
year = 2007
}