DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jung, Sukhwan | ko |
dc.contributor.author | Lai, Tuan Manh | ko |
dc.contributor.author | Segev, Aviv | ko |
dc.date.accessioned | 2016-11-09T08:12:23Z | - |
dc.date.available | 2016-11-09T08:12:23Z | - |
dc.date.created | 2016-11-04 | - |
dc.date.created | 2016-11-04 | - |
dc.date.created | 2016-11-04 | - |
dc.date.issued | 2016-06-27 | - |
dc.identifier.citation | IEEE International Big Data Congress, pp.357 - 360 | - |
dc.identifier.uri | http://hdl.handle.net/10203/213971 | - |
dc.description.abstract | The 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.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Analyzing Future Nodes in a Knowledge Network | - |
dc.type | Conference | - |
dc.identifier.wosid | 000390212200050 | - |
dc.identifier.scopusid | 2-s2.0-84994666573 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 357 | - |
dc.citation.endingpage | 360 | - |
dc.citation.publicationname | IEEE International Big Data Congress | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | San Francisco, CA | - |
dc.identifier.doi | 10.1109/BigDataCongress.2016.57 | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Segev, Aviv | - |
dc.contributor.nonIdAuthor | Lai, Tuan Manh | - |
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