PUMA publications for /author/Christian%20Bockermannhttps://puma.uni-kassel.de/author/Christian%20BockermannPUMA RSS feed for /author/Christian%20Bockermann2024-03-29T10:55:32+01:00Stream-based Community Discovery via Relational Hypergraph Factorization on Evolving Networkshttps://puma.uni-kassel.de/bibtex/2e0e77b218030558331e985746e824da0/stephandoerfelstephandoerfel2010-10-05T16:55:08+02:00metafac parafac decomposition community tensorDecomposition tensor <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christian Bockermann" itemprop="url" href="/author/Christian%20Bockermann"><span itemprop="name">C. Bockermann</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Felix Jungermann." itemprop="url" href="/author/Felix%20Jungermann."><span itemprop="name">F. Jungermann.</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen & Adaptivitaet</span>, </em></span><em>Kassel, Germany, </em>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)Tue Oct 05 16:55:08 CEST 2010Kassel, GermanyProceedings of LWA2010 - Workshop-Woche: Lernen, Wissen {\&} Adaptivitaetlwa2010Stream-based Community Discovery via Relational Hypergraph Factorization on Evolving Networks2010metafac parafac decomposition community tensorDecomposition tensor The discovery of communities or interrelations in social networks has become an important area of research. The increasing amount of information available in these networks and its decreasing life-time poses tight constraints on the information processing – storage of the data is often prohibited due to its sheer volume. In this paper we adapt a flexible approach for community discovery offering the integration of new information into the model. The continuous integration is combined with a time-based weighting of the data allowing for disposing obsolete information from the model building process. We demonstrate the usefulness of our approach by applying it on the popular Twitter network. The proposed solution can be directly fed with streaming data from Twitter, providing an up-todate community model.