Network properties of folksonomies

  • Authors:

  • Ciro Cattuto_aff3n2;Christoph Schmitz;Andrea Baldassarri;Vito D. P. Servedio_aff2n3;Vittorio Loreto_aff2n3;Andreas Hotho;Miranda Grahl;Gerd Stumme

  • Affiliations:

  • af3 Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Compendio Viminale, I-00184 Roma, Italy;Knowledge & Data Engineering Group, Dept. of Mathematics and Computer Science, Univ. of Kassel, Wilhelmshööher Allee 73, D-34121 Kassel, Germany. E-mail: {lastname}@cs.uni-kassel.de;af2 Dipartimento di Fisica, Università di Roma “La Sapienza”, P. le A. Moro, 2, I-00185 Roma, Italy. E-mail: {firstname.lastname}@roma1.infn.it;af2 Dipartimento di Fisica, Università di Roma “La Sapienza”, P. le A. Moro, 2, I-00185 Roma, Italy. E-mail: {firstname.lastname}@roma1.infn.it;af2 Dipartimento di Fisica, Università di Roma “La Sapienza”, P. le A. Moro, 2, I-00185 Roma, Italy. E-mail: {firstname.lastname}@roma1.infn.it;Knowledge & Data Engineering Group, Dept. of Mathematics and Computer Science, Univ. of Kassel, Wilhelmshööher Allee 73, D-34121 Kassel, Germany. E-mail: {lastname}@cs.uni-kassel.de;Knowledge & Data Engineering Group, Dept. of Mathematics and Computer Science, Univ. of Kassel, Wilhelmshööher Allee 73, D-34121 Kassel, Germany. E-mail: {lastname}@cs.uni-kassel.de;Knowledge & Data Engineering Group, Dept. of Mathematics and Computer Science, Univ. of Kassel, Wilhelmshööher Allee 73, D-34121 Kassel, Germany. E-mail: {lastname}@cs.uni-kassel.de

  • Venue:
  • AI Communications - Network Analysis in Natural Sciences and Engineering
  • Year:
  • 2007

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Abstract

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 of these systems. We consider their underlying data structures - so-called folksonomies - as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering 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.