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dc.contributor.authorAdrian, Kemo-
dc.contributor.authorChocron, Paula-
dc.contributor.authorConfalonieri, Roberto-
dc.contributor.authorFerrer, Xavier-
dc.contributor.authorGiráldez-Crú, Jesús-
dc.date.accessioned2017-10-04T12:40:25Z-
dc.date.available2017-10-04T12:40:25Z-
dc.date.issued2016-10-19-
dc.identifierdoi: 10.3233/978-1-61499-696-5-187-
dc.identifierissn: 09226389-
dc.identifierisbn: 978-161499695-8-
dc.identifier.citation19th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2016; Frontiers in Artificial Intelligence and Applications, V. 288, 2016: 187-196-
dc.identifier.urihttp://hdl.handle.net/10261/156073-
dc.description.abstractStudying the prediction of new links in evolutionary networks is a captivating question that has received the interest of different disciplines. Link prediction allows to extract missing information and evaluate network dynamics. Some algorithms that tackle this problem with good performances are based on the sociability index, a measure of node interactions over time. In this paper, we present a case study of this predictor in the evolutionary graph that represents the CCIA co-authorship network from 2005 to 2015. Moreover, we present a generalized version of this sociability index, that takes into account the time in which such interactions occur. We show that this new index outperforms existing predictors. Finally, we use it in order to predict new co-authorships for CCIA 2016. © 2016 The authors and IOS Press. All rights reserved.-
dc.publisherIOS Press-
dc.rightsclosedAccess-
dc.subjectEvolutionary networks-
dc.subjectLink prediction-
dc.subjectCCIA coauthorship network-
dc.titleLink Prediction in Evolutionary Graphs - The Case Study of the CCIA Network-
dc.typeartículo-
dc.identifier.doi10.3233/978-1-61499-696-5-187-
dc.date.updated2017-10-04T12:40:27Z-
dc.description.versionPeer Reviewed-
dc.language.rfc3066eng-
dc.relation.csic-
Appears in Collections:(IIIA) Comunicaciones congresos
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