PUMA publications for /user/folke/communehttps://puma.uni-kassel.de/user/folke/communePUMA RSS feed for /user/folke/commune2024-03-19T03:38:43+01:00Knowledge Acquisition Via Incremental Conceptual Clusteringhttps://puma.uni-kassel.de/bibtex/20edbe48f91025efea4af0a1a62433e42/folkefolke2010-05-04T08:55:46+02:00COMMUNE classit clustering community coweb detection <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Douglas H. Fisher" itemprop="url" href="/author/Douglas%20H.%20Fisher"><span itemprop="name">D. Fisher</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>Machine Learning</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">2 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">139--172</span></em> </span>(<em><span>September 1987<meta content="September 1987" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Machine Learning#sep#2139--172Knowledge Acquisition Via Incremental Conceptual Clustering21987COMMUNE classit clustering community coweb detection Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety of domains.
ER -Extending the definition of modularity to directed graphs with
overlapping communitieshttps://puma.uni-kassel.de/bibtex/27d8bb9ffc0402259940814addb6954c5/folkefolke2010-05-04T08:55:46+02:00COMMUNE community directed graph modularity network overlapping <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="V. Nicosia" itemprop="url" href="/author/V.%20Nicosia"><span itemprop="name">V. Nicosia</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="G. Mangioni" itemprop="url" href="/author/G.%20Mangioni"><span itemprop="name">G. Mangioni</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="V. Carchiolo" itemprop="url" href="/author/V.%20Carchiolo"><span itemprop="name">V. Carchiolo</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Malgeri" itemprop="url" href="/author/M.%20Malgeri"><span itemprop="name">M. Malgeri</span></a></span>. </span>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)<em>cite arxiv:0801.1647
Comment: 22 pages, 11 figures.</em>Tue May 04 08:55:46 CEST 2010cite arxiv:0801.1647
Comment: 22 pages, 11 figuresExtending the definition of modularity to directed graphs with
overlapping communities2008COMMUNE community directed graph modularity network overlapping Complex networks topologies present interesting and surprising properties,
such as community structures, which can be exploited to optimize communication,
to find new efficient and context-aware routing algorithms or simply to
understand the dynamics and meaning of relationships among nodes. Complex
networks are gaining more and more importance as a reference model and are a
powerful interpretation tool for many different kinds of natural, biological
and social networks, where directed relationships and contextual belonging of
nodes to many different communities is a matter of fact. This paper starts from
the definition of modularity function, given by M. Newman to evaluate the
goodness of network community decompositions, and extends it to the more
general case of directed graphs with overlapping community structures.
Interesting properties of the proposed extension are discussed, a method for
finding overlapping communities is proposed and results of its application to
benchmark case-studies are reported. We also propose a new dataset which could
be used as a reference benchmark for overlapping community structures
identification.
A spectral clustering-based framework for detecting community structures in complex networkshttps://puma.uni-kassel.de/bibtex/2d9a603d42a7379d13d8a04404bb951cc/folkefolke2010-05-04T08:55:46+02:00COMMUNE clustering community detection spectral <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 networks222009COMMUNE clustering community detection spectral 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.Community Structure in Directed Networkshttps://puma.uni-kassel.de/bibtex/293726cc0540f75ee1cb515b2923d69e8/folkefolke2010-05-04T08:55:46+02:00COMMUNE community detection directed graph network structure <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="E. A. Leicht" itemprop="url" href="/author/E.%20A.%20Leicht"><span itemprop="name">E. Leicht</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. E. J. Newman" itemprop="url" href="/author/M.%20E.%20J.%20Newman"><span itemprop="name">M. Newman</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>Phys. Rev. Lett.</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">100 </span></span>(<span itemprop="issueNumber">11</span>):
<span itemprop="pagination">118703</span></em> </span>(<em><span>März 2008<meta content="März 2008" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Phys. Rev. Lett.mar11118703Community Structure in Directed Networks1002008COMMUNE community detection directed graph network structure Analysis of weighted networkshttps://puma.uni-kassel.de/bibtex/246b762f4dd718649d5024ead4b6fdf06/folkefolke2010-05-04T08:55:46+02:00COMMUNE community detection graphs modularity networks weighted <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. E. J. Newman" itemprop="url" href="/author/M.%20E.%20J.%20Newman"><span itemprop="name">M. Newman</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>Phys. Rev. E</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">70 </span></span>(<span itemprop="issueNumber">5</span>):
<span itemprop="pagination">056131</span></em> </span>(<em><span>November 2004<meta content="November 2004" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Phys. Rev. Enov5056131Analysis of weighted networks702004COMMUNE community detection graphs modularity networks weighted On Modularity Clusteringhttps://puma.uni-kassel.de/bibtex/29e2e5f9d06d2f83be98083175560c835/folkefolke2010-05-04T08:55:46+02:00COMMUNE clustering community detection modularity <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="U. Brandes" itemprop="url" href="/author/U.%20Brandes"><span itemprop="name">U. Brandes</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="D. Delling" itemprop="url" href="/author/D.%20Delling"><span itemprop="name">D. Delling</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Gaertler" itemprop="url" href="/author/M.%20Gaertler"><span itemprop="name">M. Gaertler</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Gorke" itemprop="url" href="/author/R.%20Gorke"><span itemprop="name">R. Gorke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Hoefer" itemprop="url" href="/author/M.%20Hoefer"><span itemprop="name">M. Hoefer</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Z. Nikoloski" itemprop="url" href="/author/Z.%20Nikoloski"><span itemprop="name">Z. Nikoloski</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="D. Wagner" itemprop="url" href="/author/D.%20Wagner"><span itemprop="name">D. Wagner</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>Knowledge and Data Engineering, IEEE Transactions on</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">20 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">172 -188</span></em> </span>(<em><span>Februar 2008<meta content="Februar 2008" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Knowledge and Data Engineering, IEEE Transactions onfeb. 2172 -188On Modularity Clustering202008COMMUNE clustering community detection modularity Modularity is a recently introduced quality measure for graph clusterings. It has immediately received considerable attention in several disciplines, particularly in the complex systems literature, although its properties are not well understood. We study the problem of finding clusterings with maximum modularity, thus providing theoretical foundations for past and present work based on this measure. More precisely, we prove the conjectured hardness of maximizing modularity both in the general case and with the restriction to cuts and give an Integer Linear Programming formulation. This is complemented by first insights into the behavior and performance of the commonly applied greedy agglomerative approach.