Cannibalism in medical topic networks

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dc.contributor.authorChae, Suhyunko
dc.contributor.authorSegev, Avivko
dc.contributor.authorLee, Uichinko
dc.date.accessioned2016-10-07T09:27:23Z-
dc.date.available2016-10-07T09:27:23Z-
dc.date.created2016-10-05-
dc.date.created2016-10-05-
dc.date.created2016-10-05-
dc.date.created2016-10-05-
dc.date.issued2016-09-
dc.identifier.citationKNOWLEDGE-BASED SYSTEMS, v.108, pp.168 - 178-
dc.identifier.issn0950-7051-
dc.identifier.urihttp://hdl.handle.net/10203/213218-
dc.description.abstractAnalyzing research activities over time can give insight into the research trend and knowledge structure of a domain. Research publication activity of a topic can be measured by a network of keyword terms and their relations in the specific area. The paper analyzes medical topic networks to interpret how clusters and keyword terms change over time. Keywords are extracted from 9730,671 research publications of twenty medical topics over 40 years. Experiments show there is cannibalism which occurs when one cluster is consumed into other clusters of medical topic networks in 50% of the medical topics analyzed. The decrease of modularity values of cannibalism topics shows that research topics collaborate actively and that multidisciplinary fields have emerged over time. (C) 2016 Elsevier B.V. All rights reserved-
dc.languageEnglish-
dc.publisherELSEVIER SCIENCE BV-
dc.titleCannibalism in medical topic networks-
dc.typeArticle-
dc.identifier.wosid000382592600017-
dc.identifier.scopusid2-s2.0-84969581395-
dc.type.rimsART-
dc.citation.volume108-
dc.citation.beginningpage168-
dc.citation.endingpage178-
dc.citation.publicationnameKNOWLEDGE-BASED SYSTEMS-
dc.identifier.doi10.1016/j.knosys.2016.05.017-
dc.contributor.localauthorSegev, Aviv-
dc.contributor.localauthorLee, Uichin-
dc.contributor.nonIdAuthorChae, Suhyun-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorResearch publication activity-
dc.subject.keywordAuthorNetwork evolution-
dc.subject.keywordAuthorKeyword extraction-
dc.subject.keywordAuthorKnowledge structure-
dc.subject.keywordAuthorMedical domain-
dc.subject.keywordPlusSOCIAL NETWORKS-
dc.subject.keywordPlusCOMMUNITY-
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