Preprint
Network properties of folksonomies
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 â so-called folksonomies
â 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 cooccurrence
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.
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 â so-called folksonomies
â 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 cooccurrence
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.
Citation
@article{urn:nbn:de:hebis:34-2009042027044,
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},
title={Network properties of folksonomies},
year={2007}
}
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3010 Baldassarri, Andrea
3010 Servedio, Vito D. P.
3010 Loreto, Vittorio
3010 Hotho, Andreas
3010 Grahl, Miranda
3010 Stumme, Gerd
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<resource xsi:schemaLocation="http://datacite.org/schema/kernel-2.2 http://schema.datacite.org/meta/kernel-2.2/metadata.xsd"> 2009-04-20T10:09:03Z 2009-04-20T10:09:03Z 2007 urn:nbn:de:hebis:34-2009042027044 http://hdl.handle.net/123456789/2009042027044 5770237 bytes application/pdf eng Urheberrechtlich geschützt https://rightsstatements.org/page/InC/1.0/ 004 Network properties of folksonomies Preprint 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 â so-called folksonomies â 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 cooccurrence 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. open access Cattuto, Ciro Schmitz, Christoph Baldassarri, Andrea Servedio, Vito D. P. Loreto, Vittorio Hotho, Andreas Grahl, Miranda Stumme, Gerd Auch erschienen in: AI Communications. Amsterdam : IOP Press, 2007, Vol. 20, 4, S. 245-262. ISSN 0921-7126 Wissenserwerb Wissensmanagement </resource>
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