Clustering nodes in a graph is a useful general technique in data mining of large network data sets. In this context, Newman and Girvan 9 recently proposed an objective function for graph clustering called the Q function which allows automatic selection of the number of clusters. Empirically, higher values of the Q function have been shown to correlate well with good graph clusterings. In this paper we show how optimizing the Q function can be reformulated as a spectral relaxation problem and ...
%0 Generic
%1 White2005
%A White, Scott
%A Smyth, Padhraic
%D 2005
%K clustering graph master spectral toVerify
%T A Spectral Clustering Approach To Finding Communities in Graphs
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.59.8978
%X Clustering nodes in a graph is a useful general technique in data mining of large network data sets. In this context, Newman and Girvan 9 recently proposed an objective function for graph clustering called the Q function which allows automatic selection of the number of clusters. Empirically, higher values of the Q function have been shown to correlate well with good graph clusterings. In this paper we show how optimizing the Q function can be reformulated as a spectral relaxation problem and ...
@misc{White2005,
abstract = {Clustering nodes in a graph is a useful general technique in data mining of large network data sets. In this context, Newman and Girvan [9] recently proposed an objective function for graph clustering called the Q function which allows automatic selection of the number of clusters. Empirically, higher values of the Q function have been shown to correlate well with good graph clusterings. In this paper we show how optimizing the Q function can be reformulated as a spectral relaxation problem and ...},
added-at = {2010-09-21T15:49:20.000+0200},
author = {White, Scott and Smyth, Padhraic},
biburl = {https://www.bibsonomy.org/bibtex/249b3cd0c9986e40237a8cb23af5084e6/ans},
interhash = {aa8ce357b65fa68bccd8f3bc1d9919a9},
intrahash = {49b3cd0c9986e40237a8cb23af5084e6},
keywords = {clustering graph master spectral toVerify},
timestamp = {2011-03-22T23:02:17.000+0100},
title = {A Spectral Clustering Approach To Finding Communities in Graphs},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.59.8978},
year = 2005
}