Many networks of interest in the sciences, including a variety of social and
biological networks, are found to divide naturally into communities or modules.
The problem of detecting and characterizing this community structure has
attracted considerable recent attention. One of the most sensitive detection
methods is optimization of the quality function known as "modularity" over the
possible divisions of a network, but direct application of this method using,
for instance, simulated annealing is computationally costly. Here we show that
the modularity can be reformulated in terms of the eigenvectors of a new
characteristic matrix for the network, which we call the modularity matrix, and
that this reformulation leads to a spectral algorithm for community detection
that returns results of better quality than competing methods in noticeably
shorter running times. We demonstrate the algorithm with applications to
several network data sets.
%0 Generic
%1 citeulike:686555
%A Newman, M. E. J.
%D 2006
%K clustering, web-graph
%T Modularity and community structure in networks
%U http://arxiv.org/abs/physics/0602124
%X Many networks of interest in the sciences, including a variety of social and
biological networks, are found to divide naturally into communities or modules.
The problem of detecting and characterizing this community structure has
attracted considerable recent attention. One of the most sensitive detection
methods is optimization of the quality function known as "modularity" over the
possible divisions of a network, but direct application of this method using,
for instance, simulated annealing is computationally costly. Here we show that
the modularity can be reformulated in terms of the eigenvectors of a new
characteristic matrix for the network, which we call the modularity matrix, and
that this reformulation leads to a spectral algorithm for community detection
that returns results of better quality than competing methods in noticeably
shorter running times. We demonstrate the algorithm with applications to
several network data sets.
@misc{citeulike:686555,
abstract = {Many networks of interest in the sciences, including a variety of social and
biological networks, are found to divide naturally into communities or modules.
The problem of detecting and characterizing this community structure has
attracted considerable recent attention. One of the most sensitive detection
methods is optimization of the quality function known as "modularity" over the
possible divisions of a network, but direct application of this method using,
for instance, simulated annealing is computationally costly. Here we show that
the modularity can be reformulated in terms of the eigenvectors of a new
characteristic matrix for the network, which we call the modularity matrix, and
that this reformulation leads to a spectral algorithm for community detection
that returns results of better quality than competing methods in noticeably
shorter running times. We demonstrate the algorithm with applications to
several network data sets.},
added-at = {2009-08-06T15:16:38.000+0200},
archiveprefix = {arXiv},
author = {Newman, M. E. J.},
biburl = {https://www.bibsonomy.org/bibtex/26b2ddaabe71ca6dd9bd66d7a3a097614/chato},
citeulike-article-id = {686555},
citeulike-linkout-0 = {http://arxiv.org/abs/physics/0602124},
citeulike-linkout-1 = {http://arxiv.org/pdf/physics/0602124},
eprint = {physics/0602124},
interhash = {e664336d414a1e21d89f30cc56f5e739},
intrahash = {6b2ddaabe71ca6dd9bd66d7a3a097614},
keywords = {clustering, web-graph},
month = Feb,
posted-at = {2008-05-02 16:08:43},
priority = {0},
timestamp = {2009-08-06T15:16:46.000+0200},
title = {Modularity and community structure in networks},
url = {http://arxiv.org/abs/physics/0602124},
year = 2006
}