PUMA publications for /author/U.%20Brandeshttps://puma.uni-kassel.de/author/U.%20BrandesPUMA RSS feed for /author/U.%20Brandes2024-03-19T08:34:47+01:00Visualization of bibliographic networks with a reshaped landscape metaphorhttps://puma.uni-kassel.de/bibtex/2e5e72eed2d871523dc1100f060658a1c/stummestumme2011-04-19T11:22:02+02:00bibliography graph networks sna citation bibliographic <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>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="T. Willhalm" itemprop="url" href="/author/T.%20Willhalm"><span itemprop="name">T. Willhalm</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the symposium on Data Visualisation 2002</span>, </em></span><em>Seite <span itemprop="pagination">159--ff</span>. </em><em>Aire-la-Ville, Switzerland, Switzerland, </em><em><span itemprop="publisher">Eurographics Association</span>, </em>(<em><span>2002<meta content="2002" itemprop="datePublished"/></span></em>)Tue Apr 19 11:22:02 CEST 2011Aire-la-Ville, Switzerland, SwitzerlandProceedings of the symposium on Data Visualisation 2002159--ffVISSYM '02Visualization of bibliographic networks with a reshaped landscape metaphor2002bibliography graph networks sna citation bibliographic We describe a novel approach to visualize bibliographic networks that facilitates the simultaneous identification of clusters (e.g., topic areas) and prominent entities (e.g., surveys or landmark papers). While employing the landscape metaphor proposed in several earlier works, we introduce new means to determine relevant parameters of the landscape. Moreover, we are able to compute prominent entities, clustering of entities, and the landscape's surface in a surprisingly simple and uniform way. The effectiveness of our network visualizations is illustrated on data from the graph drawing literature.Visualization of bibliographic networks with a reshaped landscape metaphorA faster algorithm for betweenness centralityhttps://puma.uni-kassel.de/bibtex/289019a800a0250d69afda3d11be16196/benzbenz2011-02-04T16:09:58+01:00betweenness_centrality fast <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><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Journal of Mathematical Sociology</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">25 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">163--177</span></em> </span>(<em><span>2001<meta content="2001" itemprop="datePublished"/></span></em>)Fri Feb 04 16:09:58 CET 2011Journal of Mathematical Sociology2163--177A faster algorithm for betweenness centrality252001betweenness_centrality fast The betweenness centrality index is essential in the analysis of social networks, but costly to compute. Currently, the fastest known algorithms require #(n ) time and #(n ) space, where n is the number of actors in the network.Experiments on graph clustering algorithmshttps://puma.uni-kassel.de/bibtex/2191613112620e6261271504e5cf992e1/folkefolke2010-05-04T08:55:46+02:00detection algorithm graph clustering community evaluation <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}2003detection algorithm graph clustering community evaluation Maximizing Modularity is hardhttps://puma.uni-kassel.de/bibtex/2b5185cbb85b90294fa15dd2e8ea53f5e/folkefolke2010-05-04T08:55:46+02:00detection community network complexity modularity npc <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. Goerke" itemprop="url" href="/author/R.%20Goerke"><span itemprop="name">R. Goerke</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>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)<em>cite arxiv:physics/0608255 Comment: 10 pages, 1 figure.</em>Tue May 04 08:55:46 CEST 2010cite arxiv:physics/0608255 Comment: 10 pages, 1 figureMaximizing Modularity is hard2006detection community network complexity modularity npc Several algorithms have been proposed to compute partitions of networks
into communities that score high on a graph clustering index called
modularity. While publications on these algorithms typically contain
experimental evaluations to emphasize the plausibility of results,
none of these algorithms has been shown to actually compute optimal
partitions. We here settle the unknown complexity status of modularity
maximization by showing that the corresponding decision version is
NP-complete in the strong sense. As a consequence, any efficient,
i.e. polynomial-time, algorithm is only heuristic and yields suboptimal
partitions on many instances.On Modularity Clusteringhttps://puma.uni-kassel.de/bibtex/29e2e5f9d06d2f83be98083175560c835/folkefolke2010-05-04T08:55:46+02:00detection clustering community COMMUNE 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 Clustering202008detection clustering community COMMUNE 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.