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dc.contributor.authorGarcia Gasulla, Dario
dc.contributor.authorCortés García, Claudio Ulises
dc.contributor.authorAyguadé Parra, Eduard
dc.contributor.authorLabarta Mancho, Jesús José
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2015-10-30T09:51:23Z
dc.date.available2015-10-30T09:51:23Z
dc.date.issued2015
dc.identifier.citationGarcia-Gasulla, D., Cortes, C., Ayguade, E., Labarta, J. Evaluating link prediction on large graphs. A: International Conference of the Catalan Association for Artificial Intelligence. "Artificial Intelligence Research and Development: Proceedings of the 18th International Conference of the Catalan Association for Artificial Intelligence". Valencia: IOS Press, 2015, p. 90-99.
dc.identifier.isbn978-1-61499-578-4
dc.identifier.urihttp://hdl.handle.net/2117/78545
dc.description.abstractExploiting network data (i.e., graphs) is a rather particular case of data mining. The size and relevance of network domains justifies research on graph mining, but also brings forth severe complications. Computational aspects like scalability and parallelism have to be reevaluated, and well as certain aspects of the data mining process. One of those are the methodologies used to evaluate graph mining methods, particularly when processing large graphs. In this paper we focus on the evaluation of a graph mining task known as Link Prediction. First we explore the available solutions in traditional data mining for that purpose, discussing which methods are most appropriate. Once those are identified, we argue about their capabilities and limitations for producing a faithful and useful evaluation. Finally, we introduce a novel modification to a traditional evaluation methodology with the goal of adapting it to the problem of Link Prediction on large graphs.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherIOS Press
dc.subjectÀrees temàtiques de la UPC::Informàtica::Sistemes d'informació
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshData mining
dc.subject.otherGraph mining
dc.subject.otherLink prediction
dc.subject.otherEvaluation methodology
dc.titleEvaluating link prediction on large graphs
dc.typeConference report
dc.subject.lemacMineria de dades
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.identifier.doi10.3233/978-1-61499-578-4-90
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ebooks.iospress.nl/volumearticle/40922
dc.rights.accessOpen Access
local.identifier.drac16694366
dc.description.versionPostprint (author’s final draft)
local.citation.authorGarcia-Gasulla, D.; Cortes, C.; Ayguade, E.; Labarta, J.
local.citation.contributorInternational Conference of the Catalan Association for Artificial Intelligence
local.citation.pubplaceValencia
local.citation.publicationNameArtificial Intelligence Research and Development: Proceedings of the 18th International Conference of the Catalan Association for Artificial Intelligence
local.citation.startingPage90
local.citation.endingPage99


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