PUMA publications for /tag/detection%20graphhttps://puma.uni-kassel.de/tag/detection%20graphPUMA RSS feed for /tag/detection%20graph2024-03-29T15:27:28+01:00Can Entities be Friends?https://puma.uni-kassel.de/bibtex/2f22943239296ada0dfa11c30c5b4904a/03427790034277902016-06-13T12:55:35+02:00data detection entity graph linked relation web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bernardo Pereira Nunes" itemprop="url" href="/author/Bernardo%20Pereira%20Nunes"><span itemprop="name">B. Pereira Nunes</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ricardo Kawase" itemprop="url" href="/author/Ricardo%20Kawase"><span itemprop="name">R. Kawase</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stefan Dietze" itemprop="url" href="/author/Stefan%20Dietze"><span itemprop="name">S. Dietze</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Davide Taibi" itemprop="url" href="/author/Davide%20Taibi"><span itemprop="name">D. Taibi</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Marco Antonio Casanova" itemprop="url" href="/author/Marco%20Antonio%20Casanova"><span itemprop="name">M. Casanova</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Wolfgang Nejdl" itemprop="url" href="/author/Wolfgang%20Nejdl"><span itemprop="name">W. Nejdl</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the Web of Linked Entities Workshop in conjuction with the 11th International Semantic Web Conference</span>, </em></span><em>Volume 906 von CEUR-WS.org, </em><em>Seite <span itemprop="pagination">45--57</span>. </em>(<em><span>November 2012<meta content="November 2012" itemprop="datePublished"/></span></em>)Mon Jun 13 12:55:35 CEST 2016Proceedings of the Web of Linked Entities Workshop in conjuction with the 11th International Semantic Web Conferencenov45--57CEUR-WS.orgCan Entities be Friends?9062012data detection entity graph linked relation web The richness of the (Semantic) Web lies in its ability to link related resources as well as data across the Web. However, while relations within particular datasets are often well defined, links between disparate datasets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference datasets, such as Freebase and DBpedia for annotating and enriching datasets as well as document corpora, opens up opportunities to exploit their inherent semantics to uncover semantic relationships between disparate resources. In this paper, we present an approach to uncover relationships between disparate entities by analyzing the graphs of used reference datasets. We adapt a relationship assessment methodology from social network theory to measure the connectivity between entities in reference datasets and exploit these measures to identify correlated Web resources. Finally, we present an evaluation of our approach using the publicly available datasets Bibsonomy and USAToday. Can Entities be Friends?https://puma.uni-kassel.de/bibtex/2f22943239296ada0dfa11c30c5b4904a/jaeschkejaeschke2012-11-21T17:44:29+01:00data detection entity graph linked relation web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bernardo Pereira Nunes" itemprop="url" href="/author/Bernardo%20Pereira%20Nunes"><span itemprop="name">B. Pereira Nunes</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ricardo Kawase" itemprop="url" href="/author/Ricardo%20Kawase"><span itemprop="name">R. Kawase</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stefan Dietze" itemprop="url" href="/author/Stefan%20Dietze"><span itemprop="name">S. Dietze</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Davide Taibi" itemprop="url" href="/author/Davide%20Taibi"><span itemprop="name">D. Taibi</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Marco Antonio Casanova" itemprop="url" href="/author/Marco%20Antonio%20Casanova"><span itemprop="name">M. Casanova</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Wolfgang Nejdl" itemprop="url" href="/author/Wolfgang%20Nejdl"><span itemprop="name">W. Nejdl</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the Web of Linked Entities Workshop in conjuction with the 11th International Semantic Web Conference</span>, </em></span><em>Volume 906 von CEUR-WS.org, </em><em>Seite <span itemprop="pagination">45--57</span>. </em>(<em><span>November 2012<meta content="November 2012" itemprop="datePublished"/></span></em>)Wed Nov 21 17:44:29 CET 2012Proceedings of the Web of Linked Entities Workshop in conjuction with the 11th International Semantic Web Conferencenov45--57CEUR-WS.orgCan Entities be Friends?9062012data detection entity graph linked relation web The richness of the (Semantic) Web lies in its ability to link related resources as well as data across the Web. However, while relations within particular datasets are often well defined, links between disparate datasets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference datasets, such as Freebase and DBpedia for annotating and enriching datasets as well as document corpora, opens up opportunities to exploit their inherent semantics to uncover semantic relationships between disparate resources. In this paper, we present an approach to uncover relationships between disparate entities by analyzing the graphs of used reference datasets. We adapt a relationship assessment methodology from social network theory to measure the connectivity between entities in reference datasets and exploit these measures to identify correlated Web resources. Finally, we present an evaluation of our approach using the publicly available datasets Bibsonomy and USAToday. Finding community structure in networks using the eigenvectors of matriceshttps://puma.uni-kassel.de/bibtex/2090a24e34da3d0ab3d14d61dd3ad3285/folkefolke2010-05-04T08:55:46+02:00community detection graph modularity spectral theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="MEJ Newman" itemprop="url" href="/author/MEJ%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>Physical Review E</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">74 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">36104</span></em> </span>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Physical Review E336104{Finding community structure in networks using the eigenvectors of matrices}742006community detection graph modularity spectral theory Co-clustering documents and words using bipartite spectral graph partitioninghttps://puma.uni-kassel.de/bibtex/2f07d9cc4813f3ecda75e6f0c8025cece/folkefolke2010-05-04T08:55:46+02:00community detection graph spectral theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Inderjit S. Dhillon" itemprop="url" href="/author/Inderjit%20S.%20Dhillon"><span itemprop="name">I. Dhillon</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining</span>, </em></span><em>Seite <span itemprop="pagination">269--274</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM Press</span>, </em>(<em><span>2001<meta content="2001" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010New York, NY, USAKDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining269--274Co-clustering documents and words using bipartite spectral graph partitioning2001community detection graph spectral theory Spectral K-way ratio-cut partitioning and clustering.https://puma.uni-kassel.de/bibtex/29aabfb2ef97763db1ae308576b8c0258/folkefolke2010-05-04T08:55:46+02:00community detection graph partitioning spectral theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Pak K. Chan" itemprop="url" href="/author/Pak%20K.%20Chan"><span itemprop="name">P. Chan</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martine D. F. Schlag" itemprop="url" href="/author/Martine%20D.%20F.%20Schlag"><span itemprop="name">M. Schlag</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jason Y. Zien" itemprop="url" href="/author/Jason%20Y.%20Zien"><span itemprop="name">J. Zien</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>IEEE Trans. on CAD of Integrated Circuits and Systems</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">13 </span></span>(<span itemprop="issueNumber">9</span>):
<span itemprop="pagination">1088-1096</span></em> </span>(<em><span>1994<meta content="1994" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010IEEE Trans. on CAD of Integrated Circuits and Systems91088-1096Spectral K-way ratio-cut partitioning and clustering.131994community detection graph partitioning spectral theory Tag recommendations based on tensor dimensionality reductionhttps://puma.uni-kassel.de/bibtex/2e93afe409833a632af02290bbe134cba/folkefolke2010-05-04T08:55:46+02:00community detection graph recommender spectral tag theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Panagiotis Symeonidis" itemprop="url" href="/author/Panagiotis%20Symeonidis"><span itemprop="name">P. Symeonidis</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexandros Nanopoulos" itemprop="url" href="/author/Alexandros%20Nanopoulos"><span itemprop="name">A. Nanopoulos</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yannis Manolopoulos" itemprop="url" href="/author/Yannis%20Manolopoulos"><span itemprop="name">Y. Manolopoulos</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems</span>, </em></span><em>Seite <span itemprop="pagination">43--50</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010New York, NY, USARecSys '08: Proceedings of the 2008 ACM conference on Recommender systems43--50Tag recommendations based on tensor dimensionality reduction2008community detection graph recommender spectral tag theory A spectral clustering approach to finding communities in graphhttps://puma.uni-kassel.de/bibtex/2310763d5fe7195d89883c91c90681e03/folkefolke2010-05-04T08:55:46+02:00community detection graph spectral theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. White" itemprop="url" href="/author/S.%20White"><span itemprop="name">S. White</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="P. Smyth" itemprop="url" href="/author/P.%20Smyth"><span itemprop="name">P. Smyth</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"> </span>(<em><span>2005<meta content="2005" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010SIAM Data Mining{A spectral clustering approach to finding communities in graph}2005community detection graph spectral theory Modularity and community structure in networkshttps://puma.uni-kassel.de/bibtex/29104cb1678a39c96b06ed838a8aa3a63/folkefolke2010-05-04T08:55:46+02:00community detection graph modularity spectral theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="MEJ Newman" itemprop="url" href="/author/MEJ%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>Proceedings of the National Academy of Sciences</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">103 </span></span>(<span itemprop="issueNumber">23</span>):
<span itemprop="pagination">8577--8582</span></em> </span>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Proceedings of the National Academy of Sciences238577--8582{Modularity and community structure in networks}1032006community detection graph modularity spectral theory Spectral partitioning: The more eigenvectors, the betterhttps://puma.uni-kassel.de/bibtex/213da262902e2a4425f7799a519163b99/folkefolke2010-05-04T08:55:46+02:00community detection graph spectral theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Charles J. Alpert" itemprop="url" href="/author/Charles%20J.%20Alpert"><span itemprop="name">C. Alpert</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andrew B. Kahng" itemprop="url" href="/author/Andrew%20B.%20Kahng"><span itemprop="name">A. Kahng</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="So zen Yao" itemprop="url" href="/author/So%20zen%20Yao"><span itemprop="name">S. zen Yao</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. ACM/IEEE Design Automation Conf</span>, </em></span><em>Seite <span itemprop="pagination">195--200</span>. </em>(<em><span>1995<meta content="1995" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Proc. ACM/IEEE Design Automation Conf195--200Spectral partitioning: The more eigenvectors, the better1995community detection graph spectral theory A survey of kernel and spectral methods for clusteringhttps://puma.uni-kassel.de/bibtex/230fe8946a31d33d0fa81c16ec04287aa/folkefolke2010-05-04T08:55:46+02:00community detection graph kernel spectral survey theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Filippone" itemprop="url" href="/author/M.%20Filippone"><span itemprop="name">M. Filippone</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="F. Camastra" itemprop="url" href="/author/F.%20Camastra"><span itemprop="name">F. Camastra</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="F. Masulli" itemprop="url" href="/author/F.%20Masulli"><span itemprop="name">F. Masulli</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Rovetta" itemprop="url" href="/author/S.%20Rovetta"><span itemprop="name">S. Rovetta</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>Pattern recognition</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">41 </span></span>(<span itemprop="issueNumber">1</span>):
<span itemprop="pagination">176--190</span></em> </span>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Pattern recognition1176--190{A survey of kernel and spectral methods for clustering}412008community detection graph kernel spectral survey theory A comparison of spectral clustering algorithmshttps://puma.uni-kassel.de/bibtex/2829d5543415a8d8cd6c0db75c025c9d3/folkefolke2010-05-04T08:55:46+02:00community detection graph spectral theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="D. Verma" itemprop="url" href="/author/D.%20Verma"><span itemprop="name">D. Verma</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Meila" itemprop="url" href="/author/M.%20Meila"><span itemprop="name">M. Meila</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>University of Washington, Tech. Rep. UW-CSE-03-05-01</em></span></span> </span>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010University of Washington, Tech. Rep. UW-CSE-03-05-01{A comparison of spectral clustering algorithms}2003community detection graph spectral theory A tutorial on spectral clusteringhttps://puma.uni-kassel.de/bibtex/22f579735afb8bbba1da168f1b83e10c7/folkefolke2010-05-04T08:55:46+02:00community detection graph spectral theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ulrike Luxburg" itemprop="url" href="/author/Ulrike%20Luxburg"><span itemprop="name">U. Luxburg</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>Statistics and Computing</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">17 </span></span>(<span itemprop="issueNumber">4</span>):
<span itemprop="pagination">395--416</span></em> </span>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Hingham, MA, USAStatistics and Computing4395--416A tutorial on spectral clustering172007community detection graph spectral theory Spectral measures of bipartivity in complex networkshttps://puma.uni-kassel.de/bibtex/22a2cfdf7b25c7fe86be3ea81aa2324e2/folkefolke2010-05-04T08:55:46+02:00bipartite community detection graph modularity spectral theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="E. Estrada" itemprop="url" href="/author/E.%20Estrada"><span itemprop="name">E. Estrada</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J.A. Rodr\'ıguez-Velázquez" itemprop="url" href="/author/J.A.%20Rodr%7b%5c'%5ci%7dguez-Vel%7b%5c'a%7dzquez"><span itemprop="name">J. Rodr\'ıguez-Velázquez</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>SIAM Rev Phys Rev E</em></span></span> </span>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010SIAM Rev Phys Rev E046105{Spectral measures of bipartivity in complex networks}722003bipartite community detection graph modularity spectral theory Spectral Partitioning Works: Planar Graphs and Finite Element Mesheshttps://puma.uni-kassel.de/bibtex/206b1b19e0a29a145555cb1526716c451/folkefolke2010-05-04T08:55:46+02:00clustering community detection graph spectral survey theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Daniel A. Spielman" itemprop="url" href="/author/Daniel%20A.%20Spielman"><span itemprop="name">D. Spielman</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Shang Teng" itemprop="url" href="/author/Shang%20Teng"><span itemprop="name">S. Teng</span></a></span>. </span><em>Berkeley, CA, USA, </em>(<em><span>1996<meta content="1996" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Berkeley, CA, USASpectral Partitioning Works: Planar Graphs and Finite Element Meshes1996clustering community detection graph spectral survey theory A Unified View of Kernel k-means, Spectral Clustering and Graph Cutshttps://puma.uni-kassel.de/bibtex/221707c7469b7d2d19d388e729b753d91/folkefolke2010-05-04T08:55:46+02:00community detection graph k-means spectral theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Inderjit S. Dhillon" itemprop="url" href="/author/Inderjit%20S.%20Dhillon"><span itemprop="name">I. Dhillon</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yuqiang Guan" itemprop="url" href="/author/Yuqiang%20Guan"><span itemprop="name">Y. Guan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Brian Kulis" itemprop="url" href="/author/Brian%20Kulis"><span itemprop="name">B. Kulis</span></a></span>. </span><em>TR-04-25. </em><em><span itemprop="producer">University of Texas Dept. of Computer Science</span>, </em>(<em><span>2005<meta content="2005" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010TR-04-25A Unified View of Kernel k-means, Spectral Clustering and Graph Cuts2005community detection graph k-means spectral theory Recently, a variety of clustering algorithms have been proposed to handle data that is not linearly separable. Spectral clustering and kernel k-means are two such methods that are seemingly quite different. In this paper, we show that a general weighted kernel k-means objective is mathematically equivalent to a weighted graph partitioning objective. Special cases of this graph partitioning objective include ratio cut, normalized cut and ratio association. Our equivalence has important consequences: the weighted kernel k-means algorithm may be used to directly optimize the graph partitioning objectives, and conversely, spectral methods may be used to optimize the weighted kernel k-means objective. Hence, in cases where eigenvector computation is prohibitive, we eliminate the need for any eigenvector computation for graph partitioning. Moreover, we show that the Kernighan-Lin objective can also be incorporated into our framework, leading to an incremental weighted kernel k-means algorithm for local optim ization of the objective. We further discuss the issue of convergence of weighted kernel k-means for an arbitrary graph affinity matrix and provide a number of experimental results. These results show that non-spectral methods for graph partitioning are as effective as spectral methods and can be used for problems such as image segmentation in addition to data clustering. Lower bounds for the partitioning of graphshttps://puma.uni-kassel.de/bibtex/27cb789bd22dfa8ccdd2abdd30121dfc9/folkefolke2010-05-04T08:55:46+02:00clustering community detection graph spectral theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="W.E. Donath" itemprop="url" href="/author/W.E.%20Donath"><span itemprop="name">W. Donath</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A.J. Hoffman" itemprop="url" href="/author/A.J.%20Hoffman"><span itemprop="name">A. Hoffman</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>IBM Journal of Research and Development</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">17 </span></span>(<span itemprop="issueNumber">5</span>):
<span itemprop="pagination">420--425</span></em> </span>(<em><span>1973<meta content="1973" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010IBM Journal of Research and Development5420--425{Lower bounds for the partitioning of graphs}171973clustering community detection graph spectral theory On spectral clustering: Analysis and an algorithmhttps://puma.uni-kassel.de/bibtex/27485849e42418ee5ceefb45dc6eb603c/folkefolke2010-05-04T08:55:46+02:00clustering community detection graph spectral theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andrew Y. Ng" itemprop="url" href="/author/Andrew%20Y.%20Ng"><span itemprop="name">A. Ng</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Michael I. Jordan" itemprop="url" href="/author/Michael%20I.%20Jordan"><span itemprop="name">M. Jordan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yair Weiss" itemprop="url" href="/author/Yair%20Weiss"><span itemprop="name">Y. Weiss</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Advances in Neural Information Processing Systems 14</span>, </em></span><em>Seite <span itemprop="pagination">849--856</span>. </em><em><span itemprop="publisher">MIT Press</span>, </em>(<em><span>2001<meta content="2001" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Advances in Neural Information Processing Systems 14849--856On spectral clustering: Analysis and an algorithm2001clustering community detection graph spectral theory Despite many empirical successes of spectral clustering methods| algorithms that cluster points using eigenvectors of matrices derived from the data|there are several unresolved issues. First, there are a wide variety of algorithms that use the eigenvectors in slightly dierent ways. Second, many of these algorithms have no proof that they will actually compute a reasonable clustering. In this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze the algorithm, and give conditions under which it can be expected to do well. We also show surprisingly good experimental results on a number of challenging clustering problems. 1Multilevel hypergraph partitioning: Application in VLSI domainhttps://puma.uni-kassel.de/bibtex/2bdfb6003e7a8786b0a5649bb250c0a77/folkefolke2010-05-04T08:55:46+02:00clustering community detection graph hypergraph <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="G. Karypis" itemprop="url" href="/author/G.%20Karypis"><span itemprop="name">G. Karypis</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Aggarwal" itemprop="url" href="/author/R.%20Aggarwal"><span itemprop="name">R. Aggarwal</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="V. Kumar" itemprop="url" href="/author/V.%20Kumar"><span itemprop="name">V. Kumar</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Shekhar" itemprop="url" href="/author/S.%20Shekhar"><span itemprop="name">S. Shekhar</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"> </span>(<em><span>1997<meta content="1997" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Proceedings of the 34th annual conference on Design automation526--529{Multilevel hypergraph partitioning: Application in VLSI domain}1997clustering community detection graph hypergraph Multilevel k-way Hypergraph Partitioninghttps://puma.uni-kassel.de/bibtex/2d63a73732f65ce10595e210cedda3bd1/folkefolke2010-05-04T08:55:46+02:00clustering community detection graph hypergraph partitioning <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="George Karypis" itemprop="url" href="/author/George%20Karypis"><span itemprop="name">G. Karypis</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Vipin Kumar" itemprop="url" href="/author/Vipin%20Kumar"><span itemprop="name">V. Kumar</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">In Proceedings of the Design and Automation Conference</span>, </em></span><em>Seite <span itemprop="pagination">343--348</span>. </em>(<em><span>1998<meta content="1998" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010In Proceedings of the Design and Automation Conference343--348Multilevel k-way Hypergraph Partitioning1998clustering community detection graph hypergraph partitioning In this paper, we present a new multilevel k-way hypergraph partitioning algorithm that substantially outperforms the existing state-of-the-art K-PM/LR algorithm for multi-way partitioning. both for optimizing local as well as global objectives. Experiments on the ISPD98 benchmark suite show that the partitionings produced by our scheme are on the average 15% to 23% better than those produced by the K-PM/LR algorithm, both in terms of the hyperedge cut as well as the (K - 1) metric. Furthermore, our algorithm is significantly faster, requiring 4 to 5 times less time than that required by K-PM/LR. 1 Introduction Hypergraph partitioning is an important problem with extensive application to many areas, including VLSI design [10], efficient storage of large databases on disks [14], and data mining [13]. The problem is to partition the vertices of a hypergraph into k roughly equal parts, such that a certain objective function defined over the hyperedges is optimized. A commonly used obje...Experiments on graph clustering algorithmshttps://puma.uni-kassel.de/bibtex/2191613112620e6261271504e5cf992e1/folkefolke2010-05-04T08:55:46+02:00algorithm clustering community detection evaluation graph <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="M. Gaertler" itemprop="url" href="/author/M.%20Gaertler"><span itemprop="name">M. Gaertler</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>Lecture notes in computer science</em></span></span> </span>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Lecture notes in computer science568--579{Experiments on graph clustering algorithms}2003algorithm clustering community detection evaluation graph