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Network Properties of Folksonomies

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

In: Proc. WWW2007 Workshop "Tagging and Metadata for Social Information Organization" Banff: (May 8, 2007) .

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URL:	http://www.kde.cs.uni-kassel.de/stumme/papers/2007/schmitz07network.pdf
Additional URLs:	Extended version URL:text:

Social resource sharing systems like YouTube and del.icio.us haveacquired a large number of users within the last few years. Theyprovide rich resources for data analysis, information retrieval, andknowledge discovery applications. A first step towards this end is togain better insights into content and structure of these systems. Inthis 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 adaptclassical network measures like characteristic path length andclustering coefficient to them. Subsequently, we introduce a network of tag co-occurrence andinvestigate some of its statistical properties, focusing oncorrelations in node connectivity and pointing out features thatreflect emergent semantics within the folksonomy. We show that simplestatistical indicators unambiguously spot non-social behavior such as spam.

BibTeX record

@inproceedings{Schmitz:2007,
  abstract = {Social resource sharing systems like YouTube and del.icio.us haveacquired a large number of users within the last few years. Theyprovide rich resources for data analysis, information retrieval, andknowledge discovery applications. A first step towards this end is togain better insights into content and structure of these systems. Inthis 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 adaptclassical network measures like characteristic path length andclustering coefficient to them. Subsequently, we introduce a network of tag co-occurrence andinvestigate some of its statistical properties, focusing oncorrelations in node connectivity and pointing out features thatreflect emergent semantics within the folksonomy. We show that simplestatistical indicators unambiguously spot non-social behavior such as spam.},
  added-at = {2007-12-14T07:34:29.000+0100},
  address = {Banff},
  author = {Schmitz, Christoph and Grahl, Miranda and Hotho, Andreas and Stumme, Gerd and Catutto, Ciro and Baldassarri, Andrea and Loreto, Vittorio and Servedio, Vito D. P.},
  biburl = {http://www.bibsonomy.org/bibtex/223a0a0cd67ab0014e0346527e986caeb/diego_ma},
  booktitle = {Proc. WWW2007 Workshop "Tagging and Metadata for Social Information Organization"},
  date-added = {2007-11-16 14:43:16 -0500},
  date-modified = {2007-11-16 14:43:46 -0500},
  day = 8,
  interhash = {20bd468c1c9b71206ac6f8b67ed676d6},
  intrahash = {23a0a0cd67ab0014e0346527e986caeb},
  keywords = {folksonomy},
  month = May,
  note = {An extended version appeared in AI Communications.},
  timestamp = {2007-12-14T07:34:29.000+0100},
  title = {Network Properties of Folksonomies},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2007/schmitz07network.pdf},
  year = 2007
}

Endnote record

%0 Conference Paper
%1 Schmitz:2007
%A Schmitz, Christoph
%A Grahl, Miranda
%A Hotho, Andreas
%A Stumme, Gerd
%A Catutto, Ciro
%A Baldassarri, Andrea
%A Loreto, Vittorio
%A Servedio, Vito D. P.
%B Proc. WWW2007 Workshop "Tagging and Metadata for Social Information Organization"
%C Banff
%D 2007
%K 
%T Network Properties of Folksonomies
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2007/schmitz07network.pdf
%X Social resource sharing systems like YouTube and del.icio.us haveacquired a large number of users within the last few years. Theyprovide rich resources for data analysis, information retrieval, andknowledge discovery applications. A first step towards this end is togain better insights into content and structure of these systems. Inthis 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 adaptclassical network measures like characteristic path length andclustering coefficient to them. Subsequently, we introduce a network of tag co-occurrence andinvestigate some of its statistical properties, focusing oncorrelations in node connectivity and pointing out features thatreflect emergent semantics within the folksonomy. We show that simplestatistical indicators unambiguously spot non-social behavior such as spam.

BibSonomy :: publication ::

Network Properties of Folksonomies

Resources (URL, PDF, PS...)

Abstract

BibTeX record

Endnote record