Link Prediction with Social Vector Clocks


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Prüfsumme: MD5:42444af2894bc40c41c3a1ed3f127b1b

LEE, Conrad, Bobo NICK, Ulrik BRANDES, Pádraig CUNNINGHAM, 2013. Link Prediction with Social Vector Clocks

@unpublished{Lee2013Predi-24496, title={Link Prediction with Social Vector Clocks}, year={2013}, author={Lee, Conrad and Nick, Bobo and Brandes, Ulrik and Cunningham, Pádraig} }

<rdf:RDF xmlns:rdf="" xmlns:bibo="" xmlns:dc="" xmlns:dcterms="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:creator>Brandes, Ulrik</dc:creator> <dc:contributor>Brandes, Ulrik</dc:contributor> <dcterms:issued>2013</dcterms:issued> <dc:rights>deposit-license</dc:rights> <dc:creator>Cunningham, Pádraig</dc:creator> <dc:contributor>Nick, Bobo</dc:contributor> <dc:contributor>Lee, Conrad</dc:contributor> <dcterms:rights rdf:resource=""/> <dc:date rdf:datatype="">2013-10-11T09:33:50Z</dc:date> <dcterms:abstract xml:lang="eng">State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vector clocks, an adaptation of the vector-clock concept introduced in distributed computing to social interaction networks. In fact, our experiments suggest that by taking into account the order and spacing of interactions, social vector clocks exploit different aspects of link formation so that their combination with previous approaches yields the most accurate predictor to date.</dcterms:abstract> <dcterms:title>Link Prediction with Social Vector Clocks</dcterms:title> <dc:creator>Nick, Bobo</dc:creator> <dc:language>eng</dc:language> <dc:contributor>Cunningham, Pádraig</dc:contributor> <bibo:uri rdf:resource=""/> <dcterms:available rdf:datatype="">2013-10-11T09:33:50Z</dcterms:available> <dc:creator>Lee, Conrad</dc:creator> </rdf:Description> </rdf:RDF>

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