M. Newman. (2004)cite arxiv:cond-mat/0407503
Comment: 9 pages, 3 figures.
Abstract
The connections in many networks are not merely binary entities, either
present or not, but have associated weights that record their strengths
relative to one another. Recent studies of networks have, by and large, steered
clear of such weighted networks, which are often perceived as being harder to
analyze than their unweighted counterparts. Here we point out that weighted
networks can in many cases be analyzed using a simple mapping from a weighted
network to an unweighted multigraph, allowing us to apply standard techniques
for unweighted graphs to weighted ones as well. We give a number of examples of
the method, including an algorithm for detecting community structure in
weighted networks and a new and simple proof of the max-flow/min-cut theorem.
%0 Generic
%1 newman2004analysis
%A Newman, M. E. J.
%D 2004
%K analysis modularity network weighted
%T Analysis of weighted networks
%U http://arxiv.org/abs/cond-mat/0407503
%X The connections in many networks are not merely binary entities, either
present or not, but have associated weights that record their strengths
relative to one another. Recent studies of networks have, by and large, steered
clear of such weighted networks, which are often perceived as being harder to
analyze than their unweighted counterparts. Here we point out that weighted
networks can in many cases be analyzed using a simple mapping from a weighted
network to an unweighted multigraph, allowing us to apply standard techniques
for unweighted graphs to weighted ones as well. We give a number of examples of
the method, including an algorithm for detecting community structure in
weighted networks and a new and simple proof of the max-flow/min-cut theorem.
@misc{newman2004analysis,
abstract = { The connections in many networks are not merely binary entities, either
present or not, but have associated weights that record their strengths
relative to one another. Recent studies of networks have, by and large, steered
clear of such weighted networks, which are often perceived as being harder to
analyze than their unweighted counterparts. Here we point out that weighted
networks can in many cases be analyzed using a simple mapping from a weighted
network to an unweighted multigraph, allowing us to apply standard techniques
for unweighted graphs to weighted ones as well. We give a number of examples of
the method, including an algorithm for detecting community structure in
weighted networks and a new and simple proof of the max-flow/min-cut theorem.
},
added-at = {2011-02-02T14:12:29.000+0100},
author = {Newman, M. E. J.},
biburl = {https://www.bibsonomy.org/bibtex/27e9e26cd429f639a05bbe2652c271aa0/folke},
description = {Analysis of weighted networks},
interhash = {5e36ff53e9fd2c58615a38b5ef708a19},
intrahash = {7e9e26cd429f639a05bbe2652c271aa0},
keywords = {analysis modularity network weighted},
note = {cite arxiv:cond-mat/0407503
Comment: 9 pages, 3 figures},
timestamp = {2011-02-02T14:12:29.000+0100},
title = {Analysis of weighted networks},
url = {http://arxiv.org/abs/cond-mat/0407503},
year = 2004
}