Link prediction with social vector clocks

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LEE, Conrad, Bobo NICK, Ulrik BRANDES, Padraig CUNNINGHAM, 2013. Link prediction with social vector clocks. the 19th ACM SIGKDD international conference. Chicago, Illinois, USA, 11. Aug 2013 - 14. Aug 2013. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13. the 19th ACM SIGKDD international conference. Chicago, Illinois, USA, 11. Aug 2013 - 14. Aug 2013. New York, New York, USA:ACM Press, pp. 784. ISBN 978-1-4503-2174-7

@inproceedings{Lee2013predi-24821, title={Link prediction with social vector clocks}, year={2013}, doi={10.1145/2487575.2487615}, isbn={978-1-4503-2174-7}, address={New York, New York, USA}, publisher={ACM Press}, booktitle={Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13}, author={Lee, Conrad and Nick, Bobo and Brandes, Ulrik and Cunningham, Padraig} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/24821"> <dc:creator>Cunningham, Padraig</dc:creator> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/24821"/> <dc:creator>Brandes, Ulrik</dc:creator> <dc:contributor>Lee, Conrad</dc:contributor> <dcterms:issued>2013</dcterms:issued> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-10-11T09:46:19Z</dc:date> <dc:contributor>Nick, Bobo</dc:contributor> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <dc:contributor>Brandes, Ulrik</dc:contributor> <dc:contributor>Cunningham, Padraig</dc:contributor> <dc:rights>deposit-license</dc:rights> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-08-30T22:25:04Z</dcterms:available> <dcterms:title>Link prediction with social vector clocks</dcterms:title> <dcterms:bibliographicCitation>KDD'13 : The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ; Chicago, IL, USA - August 11 - 14, 2013 / Inderjit S. Dhillon ... (eds.). - New York : ACM, 2013. - S. 784-792. - ISBN 978-1-4503-2174-7</dcterms:bibliographicCitation> <dc:language>eng</dc:language> <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> <dc:creator>Lee, Conrad</dc:creator> <dc:creator>Nick, Bobo</dc:creator> </rdf:Description> </rdf:RDF>

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