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dc.contributor.authorAksu, Hidayeten_US
dc.contributor.authorCanım, M.en_US
dc.contributor.authorChang, Y.-C.en_US
dc.contributor.authorKörpeoğlu, İbrahimen_US
dc.contributor.authorUlusoy, Özgüren_US
dc.coverage.spatialSanta Clara, CA, USA
dc.date.accessioned2016-02-08T12:06:45Z
dc.date.available2016-02-08T12:06:45Z
dc.date.issued2013-06-07en_US
dc.identifier.urihttp://hdl.handle.net/11693/27960
dc.descriptionConference name: 2013 IEEE International Congress on Big Data
dc.descriptionDate of Conference: 27 June-2 July 2013
dc.description.abstractCommunity identification in social networks is of great interest and with dynamic changes to its graph representation and content, the incremental maintenance of community poses significant challenges in computation. Moreover, the intensity of community engagement can be distinguished at multiple levels, resulting in a multi-resolution community representation that has to be maintained over time. In this paper, we first formalize this problem using the k-core metric projected at multiple k values, so that multiple community resolutions are represented with multiple k-core graphs. We then present distributed algorithms to construct and maintain a multi-k-core graph, implemented on the scalable big-data platform Apache HBase. Our experimental evaluation results demonstrate orders of magnitude speedup by maintaining multi-k-core incrementally over complete reconstruction. Our algorithms thus enable practitioners to create and maintain communities at multiple resolutions on different topics in rich social network content simultaneously. © 2013 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.title2013 IEEE International Congress on Big Dataen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/BigData.Congress.2013.23en_US
dc.subjectBig Data analyticsen_US
dc.subjectCommunity identificationen_US
dc.subjectDistributed computingen_US
dc.subjectDynamic social networksen_US
dc.subjectk-coreen_US
dc.subjectBig datumen_US
dc.subjectCommunity engagementen_US
dc.subjectCommunity identificationen_US
dc.subjectDynamic social networksen_US
dc.subjectExperimental evaluationen_US
dc.subjectIncremental maintenanceen_US
dc.subjectMultiple resolutionsen_US
dc.subjectAlgorithmsen_US
dc.subjectDistributed computer systemsen_US
dc.subjectSocial networking (online)en_US
dc.titleMulti-resolution social network community identification and maintenance on big data platformen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage102en_US
dc.citation.epage109en_US
dc.identifier.doi10.1109/BigData.Congress.2013.23en_US
dc.publisherIEEE


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