BibSonomyburst/user/diego_ma/networksBibSonomy RSS feed for /user/diego_ma/networks2012-02-16T18:49:07+01:00http://www.bibsonomy.org/bibtex/2b536fc12998902baa7c463d2eb14301f/diego_madiego_ma2007-12-14T07:36:02+01:00folksonomy networks <span class="authorEditorList"><a href="/author/Cattuto">Ciro Cattuto</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, <a href="/author/Baldassarri">Andrea Baldassarri</a>, <a href="/author/Servedio">Vito D.P. Servedio</a>, <a href="/author/Loreto">Vittorio Loreto</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Grahl">Miranda Grahl</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>AI Communications</em> (<em>2007</em>)Fri Dec 14 07:36:02 CET 2007AI CommunicationsNetwork Properties of Folksonomies2007folksonomy networks Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two ofthe systems. We consider their underlying data structures ---so-called folksonomies --- as tri-partite hypergraphs, and adapt classical network measures like characteristic path length andclustering coefficient to them. Subsequently, we introduce a network of tag co-occurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam.