PUMA publications for /user/hotho/algorithmshttps://puma.uni-kassel.de/user/hotho/algorithmsPUMA RSS feed for /user/hotho/algorithms2024-03-29T10:21:49+01:00Prediction and ranking algorithms for event-based network datahttps://puma.uni-kassel.de/bibtex/289a23b31a476c4f3f771b5e3e4a8432c/hothohotho2010-08-02T21:22:15+02:00algorithms event prediction ranking toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Joshua O'Madadhain" itemprop="url" href="/author/Joshua%20O'Madadhain"><span itemprop="name">J. O'Madadhain</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jon Hutchins" itemprop="url" href="/author/Jon%20Hutchins"><span itemprop="name">J. Hutchins</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Padhraic Smyth" itemprop="url" href="/author/Padhraic%20Smyth"><span itemprop="name">P. Smyth</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>SIGKDD Explor. Newsl.</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">7 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">23--30</span></em> </span>(<em><span>2005<meta content="2005" itemprop="datePublished"/></span></em>)Mon Aug 02 21:22:15 CEST 2010New York, NY, USASIGKDD Explor. Newsl.223--30Prediction and ranking algorithms for event-based network data72005algorithms event prediction ranking toread Event-based network data consists of sets of events over time, each of which may involve multiple entities. Examples include email traffic, telephone calls, and research publications (interpreted as co-authorship events). Traditional network analysis techniques, such as social network models, often aggregate the relational information from each event into a single static network. In contrast, in this paper we focus on the temporal nature of such data. In particular, we look at the problems of temporal link prediction and node ranking, and describe new methods that illustrate opportunities for data mining and machine learning techniques in this context. Experimental results are discussed for a large set of co-authorship events measured over multiple years, and a large corporate email data set spanning 21 months.Prediction and ranking algorithms for event-based network data