<rdf:RDF xmlns:community="http://www.bibsonomy.org/ontologies/2008/05/community#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:admin="http://webns.net/mvcb/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:cc="http://web.resource.org/cc/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:swrc="http://swrc.ontoware.org/ontology#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xml:base="http://www.bibsonomy.org/user/ht09/components"><owl:Ontology rdf:about=""><rdfs:comment>BibSonomy publications for /user/ht09/components</rdfs:comment><owl:imports rdf:resource="http://swrc.ontoware.org/ontology/portal"/></owl:Ontology><rdf:Description rdf:about="http://www.bibsonomy.org/bibtex/2f696989e22dd4c77c8a6352526e13efe/ht09"><owl:sameAs rdf:resource="http://www.bibsonomy.org/uri/bibtex/2f696989e22dd4c77c8a6352526e13efe/ht09"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#InProceedings"/><swrc:date>Tue Jun 16 15:00:02 CEST 2009</swrc:date><swrc:address>New York, NY, USA</swrc:address><swrc:booktitle>HT &#039;09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia</swrc:booktitle><swrc:month>July</swrc:month><swrc:publisher><swrc:Organization swrc:name="ACM"/></swrc:publisher><swrc:title>Hyperincident Connected Components of Tagging Networks</swrc:title><swrc:year>2009</swrc:year><swrc:keywords>components connected detection fp105 fullPaper ht2009 spam tagging </swrc:keywords><swrc:abstract>Data created by social bookmarking systems can be described as
3-partite 3-uniform hypergraphs connecting documents, users, and
tags (tagging networks),  
such that the toolbox of complex network analysis can be applied to 
examine their properties. One of the most basic tools, the
analysis of connected components, however cannot be applied
meaningfully: Tagging networks 
tend to be almost entirely connected. We therefore propose a
generalization of connected components, m-hyperincident
connected components. 
We show that decomposing tagging networks into 2-hyperincident
connected components yields a characteristic component
distribution with a salient giant component that can be found
across various datasets.  
This pattern changes if the underlying formation process
changes, for example, if the hypergraph is constructed from
search logs, or if the tagging data is contaminated by spam: It
turns out that the second- to 129th largest components of the
spam-labeled Bibsonomy dataset are inhabited exclusively by spam
users. Based on these findings, we propose and  unsupervised
method for spam detection. </swrc:abstract><swrc:hasExtraField><swrc:Field swrc:value="Full Paper" swrc:key="session"/></swrc:hasExtraField><swrc:hasExtraField><swrc:Field swrc:value="fp105" swrc:key="paperid"/></swrc:hasExtraField><swrc:author><rdf:Seq><rdf:_1><swrc:Person swrc:name="Nicolas Neubauer"/></rdf:_1><rdf:_2><swrc:Person swrc:name="Klaus Obermayer"/></rdf:_2></rdf:Seq></swrc:author></rdf:Description></rdf:RDF>
