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Network properties of folksonomies

by: Ciro Cattuto, Christoph Schmitz, Andrea Baldassarri, Vito D. P. Servedio, Vittorio Loreto, Andreas Hotho, Miranda Grahl, and Gerd Stumme

In: AI Communications, Vol. 20, Nr. 4 Amsterdam, The Netherlands: IOS Press (December 2007) , p. 245--262.

Resources (URL, PDF, PS...)

URL:	http://portal.acm.org/citation.cfm?id=1365538

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.

BibTeX record

@article{cattuto2007network,
  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.
},
  added-at = {2010-04-21T16:27:56.000+0200},
  address = {Amsterdam, The Netherlands},
  author = {Cattuto, Ciro and Schmitz, Christoph and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio and Hotho, Andreas and Grahl, Miranda and Stumme, Gerd},
  biburl = {http://www.bibsonomy.org/bibtex/21dfe8b2aa29adf4929cbb845950f78bc/ueber},
  interhash = {fc5f2df61d28bc99b7e15029da125588},
  intrahash = {1dfe8b2aa29adf4929cbb845950f78bc},
  issn = {0921-7126},
  journal = {AI Communications},
  keywords = {folksonomies},
  month = dec,
  number = 4,
  pages = {245--262},
  publisher = {IOS Press},
  timestamp = {2010-04-21T16:27:56.000+0200},
  title = {Network properties of folksonomies},
  url = {http://portal.acm.org/citation.cfm?id=1365538},
  volume = 20,
  year = 2007
}

Endnote record

%0 Journal Article
%1 cattuto2007network
%A Cattuto, Ciro
%A Schmitz, Christoph
%A Baldassarri, Andrea
%A Servedio, Vito D. P.
%A Loreto, Vittorio
%A Hotho, Andreas
%A Grahl, Miranda
%A Stumme, Gerd
%C Amsterdam, The Netherlands
%D 2007
%I IOS Press
%J AI Communications
%K 
%N 4
%P 245--262
%T Network properties of folksonomies
%U http://portal.acm.org/citation.cfm?id=1365538
%V 20
%X 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.

BibSonomy :: publication ::

Network properties of folksonomies

Resources (URL, PDF, PS...)

Abstract

BibTeX record

Endnote record