Predicting High Impact Academic Papers Using Citation Network Features

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dc.contributor.authorMcNamara, Daniel
dc.contributor.authorWong, Paul
dc.contributor.authorChristen, Peter
dc.contributor.authorNg, Kee Siong
dc.coverage.spatialGold Coast Australia
dc.date.accessioned2015-12-10T22:59:25Z
dc.date.createdApril 14-17 2013
dc.date.issued2013
dc.date.updated2015-12-10T08:12:42Z
dc.description.abstractPredicting future high impact academic papers is of benefit to a range of stakeholders, including governments, universities, academics, and investors. Being able to predict 'the next big thing' allows the allocation of resources to fields where these rapi
dc.identifier.isbn9783642403187
dc.identifier.urihttp://hdl.handle.net/1885/61075
dc.publisherSpringer
dc.relation.ispartofseriesPacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013)
dc.sourceTrends and Applications in Knowledge Discovery and Data Mining: PAKDD 2013 Workshops
dc.source.urihttp://pakdd2013.pakdd.org/
dc.titlePredicting High Impact Academic Papers Using Citation Network Features
dc.typeConference paper
local.bibliographicCitation.lastpage25
local.bibliographicCitation.startpage14
local.contributor.affiliationMcNamara, Daniel, College of Engineering and Computer Science, ANU
local.contributor.affiliationWong, Paul, Administrative Division, ANU
local.contributor.affiliationChristen, Peter, College of Engineering and Computer Science, ANU
local.contributor.affiliationNg, Kee Siong, College of Engineering and Computer Science, ANU
local.contributor.authoruidMcNamara, Daniel, u5126673
local.contributor.authoruidWong, Paul, u9714433
local.contributor.authoruidChristen, Peter, u4021539
local.contributor.authoruidNg, Kee Siong, u9914730
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationu4326120xPUB585
local.identifier.doi10.1007/978-3-642-40319-4_2
local.identifier.scopusID2-s2.0-84892894945
local.type.statusPublished Version

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