Analyzing Future Nodes in a Knowledge Network

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dc.contributor.authorJung, Sukhwanko
dc.contributor.authorLai, Tuan Manhko
dc.contributor.authorSegev, Avivko
dc.date.accessioned2016-11-09T08:12:23Z-
dc.date.available2016-11-09T08:12:23Z-
dc.date.created2016-11-04-
dc.date.created2016-11-04-
dc.date.created2016-11-04-
dc.date.issued2016-06-27-
dc.identifier.citationIEEE International Big Data Congress, pp.357 - 360-
dc.identifier.urihttp://hdl.handle.net/10203/213971-
dc.description.abstractThe paper proposes new methods for knowledge prediction using network analytics and introduces pEgonet, sub-networks within knowledge networks consisting of to-beneighbors of new knowledge. Preliminary results show that it is feasible to predict how future knowledge is added in the knowledge network by utilizing basic properties of pEgonet. The paper presents initial work which will be expanded to derive a method to predict labelled future knowledge, with its impact and structures-
dc.languageEnglish-
dc.publisherIEEE Computer Society-
dc.titleAnalyzing Future Nodes in a Knowledge Network-
dc.typeConference-
dc.identifier.wosid000390212200050-
dc.identifier.scopusid2-s2.0-84994666573-
dc.type.rimsCONF-
dc.citation.beginningpage357-
dc.citation.endingpage360-
dc.citation.publicationnameIEEE International Big Data Congress-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationSan Francisco, CA-
dc.identifier.doi10.1109/BigDataCongress.2016.57-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorSegev, Aviv-
dc.contributor.nonIdAuthorLai, Tuan Manh-
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