PUMA publications for /author/Andreas%20W.M.%20Dresshttps://puma.uni-kassel.de/author/Andreas%20W.M.%20DressPUMA RSS feed for /author/Andreas%20W.M.%20Dress2024-03-29T11:07:35+01:00A spectral clustering-based framework for detecting community structures in complex networkshttps://puma.uni-kassel.de/bibtex/2d9a603d42a7379d13d8a04404bb951cc/folkefolke2010-05-04T08:55:46+02:00detection clustering spectral community COMMUNE <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jeffrey Q. Jiang" itemprop="url" href="/author/Jeffrey%20Q.%20Jiang"><span itemprop="name">J. Jiang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas W.M. Dress" itemprop="url" href="/author/Andreas%20W.M.%20Dress"><span itemprop="name">A. Dress</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Genke Yang" itemprop="url" href="/author/Genke%20Yang"><span itemprop="name">G. Yang</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Applied Mathematics Letters</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">22 </span></span>(<span itemprop="issueNumber">9</span>):
<span itemprop="pagination">1479 - 1482</span></em> </span>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Applied Mathematics Letters91479 - 1482A spectral clustering-based framework for detecting community structures in complex networks222009detection clustering spectral community COMMUNE 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.