Exploring recent developments in spectral clustering, we discovered that relaxing a spectral reformulation of Newman's Q-measure (a measure that may guide the search for-and help to evaluate the fit of - community structures in networks) yields a new framework for use in detecting fuzzy communities and identifying so-called unstable nodes. In this note, we present and illustrate this approach, which we expect to further enhance our understanding of the intrinsic structure of networks and of network-based clustering procedures. We applied a variation of the fuzzy k-means algorithm, an instance of our framework, to two social networks. The computational results illustrate its potential.
Description
ScienceDirect - Applied Mathematics Letters : A spectral clustering-based framework for detecting community structures in complex networks
%0 Journal Article
%1 Jiang20091479
%A Jiang, Jeffrey Q.
%A Dress, Andreas W.M.
%A Yang, Genke
%D 2009
%J Applied Mathematics Letters
%K COMMUNE clustering community detection spectral
%N 9
%P 1479 - 1482
%R 10.1016/j.aml.2009.02.005
%T A spectral clustering-based framework for detecting community structures in complex networks
%U http://www.sciencedirect.com/science/article/B6TY9-4W6XYH5-5/2/693a9ed19784792496c83e96b4fa828b
%V 22
%X Exploring recent developments in spectral clustering, we discovered that relaxing a spectral reformulation of Newman's Q-measure (a measure that may guide the search for-and help to evaluate the fit of - community structures in networks) yields a new framework for use in detecting fuzzy communities and identifying so-called unstable nodes. In this note, we present and illustrate this approach, which we expect to further enhance our understanding of the intrinsic structure of networks and of network-based clustering procedures. We applied a variation of the fuzzy k-means algorithm, an instance of our framework, to two social networks. The computational results illustrate its potential.
@article{Jiang20091479,
abstract = {Exploring recent developments in spectral clustering, we discovered that relaxing a spectral reformulation of Newman's Q-measure (a measure that may guide the search for-and help to evaluate the fit of - community structures in networks) yields a new framework for use in detecting fuzzy communities and identifying so-called unstable nodes. In this note, we present and illustrate this approach, which we expect to further enhance our understanding of the intrinsic structure of networks and of network-based clustering procedures. We applied a variation of the fuzzy k-means algorithm, an instance of our framework, to two social networks. The computational results illustrate its potential.},
added-at = {2010-04-27T10:03:34.000+0200},
author = {Jiang, Jeffrey Q. and Dress, Andreas W.M. and Yang, Genke},
biburl = {https://www.bibsonomy.org/bibtex/2d9a603d42a7379d13d8a04404bb951cc/folke},
description = {ScienceDirect - Applied Mathematics Letters : A spectral clustering-based framework for detecting community structures in complex networks},
doi = {10.1016/j.aml.2009.02.005},
interhash = {08fe9886403ff8d2564fca447aef8172},
intrahash = {d9a603d42a7379d13d8a04404bb951cc},
issn = {0893-9659},
journal = {Applied Mathematics Letters},
keywords = {COMMUNE clustering community detection spectral},
number = 9,
pages = {1479 - 1482},
timestamp = {2010-04-28T06:45:40.000+0200},
title = {A spectral clustering-based framework for detecting community structures in complex networks},
url = {http://www.sciencedirect.com/science/article/B6TY9-4W6XYH5-5/2/693a9ed19784792496c83e96b4fa828b},
volume = 22,
year = 2009
}