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dc.contributor.authorDemirci, Gündüz Vehbien_US
dc.contributor.authorFerhatosmanoğlu, H.en_US
dc.contributor.authorAykanat, Cevdeten_US
dc.date.accessioned2020-02-03T12:27:56Z
dc.date.available2020-02-03T12:27:56Z
dc.date.issued2019
dc.identifier.issn1066-8888
dc.identifier.urihttp://hdl.handle.net/11693/52999
dc.description.abstractGraph partitioning is an essential task for scalable data management and analysis. The current partitioning methods utilize the structure of the graph, and the query log if available. Some queries performed on the database may trigger further operations. For example, the query workload of a social network application may contain re-sharing operations in the form of cascades. It is beneficial to include the potential cascades in the graph partitioning objectives. In this paper, we introduce the problem of cascade-aware graph partitioning that aims to minimize the overall cost of communication among parts/servers during cascade processes. We develop a randomized solution that estimates the underlying cascades, and use it as an input for partitioning of large-scale graphs. Experiments on 17 real social networks demonstrate the effectiveness of the proposed solution in terms of the partitioning objectives.en_US
dc.language.isoEnglishen_US
dc.source.titleThe VLDB Journalen_US
dc.relation.isversionofhttps://dx.doi.org/10.1007/s00778-018-0531-8en_US
dc.subjectGraph partitioningen_US
dc.subjectInformation cascadeen_US
dc.subjectPropagation modelsen_US
dc.subjectRandomized algorithmsen_US
dc.subjectScalabilityen_US
dc.subjectSocial networksen_US
dc.titleCascade-aware partitioning of large graph databasesen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage329en_US
dc.citation.epage350en_US
dc.citation.volumeNumber28en_US
dc.citation.issueNumber3en_US
dc.identifier.doi10.1007/s00778-018-0531-8en_US
dc.publisherSpringeren_US
dc.contributor.bilkentauthorDemirci, Gündüz Vehbi
dc.contributor.bilkentauthorAykanat, Cevdet


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