Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83007
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dc.contributor.authorGuo, Taoen
dc.contributor.authorCao, Xinen
dc.contributor.authorCong, Gaoen
dc.contributor.authorLu, Jiahengen
dc.contributor.authorLin, Xueminen
dc.date.accessioned2017-05-15T03:46:18Zen
dc.date.accessioned2019-12-06T15:10:07Z-
dc.date.available2017-05-15T03:46:18Zen
dc.date.available2019-12-06T15:10:07Z-
dc.date.issued2017en
dc.identifier.citationGuo, T., Cao, X., Cong, G., Lu, J., & Lin, X. (2017). Distributed Algorithms on Exact Personalized PageRank. Proceedings of the 2017 ACM International Conference on Management of Data (SIGMOD '17), 479-494.en
dc.identifier.urihttps://hdl.handle.net/10356/83007-
dc.description16 p.en
dc.description.abstractAs one of the most well known graph computation problems, Personalized PageRank is an effective approach for computing the similarity score between two nodes, and it has been widely used in various applications, such as link prediction and recommendation. Due to the high computational cost and space cost of computing the exact Personalized PageRank Vector (PPV), most existing studies compute PPV approximately. In this paper, we propose novel and efficient distributed algorithms that compute PPV exactly based on graph partitioning on a general coordinator-based share-nothing distributed computing platform. Our algorithms takes three aspects into account: the load balance, the communication cost, and the computation cost of each machine. The proposed algorithms only require one time of communication between each machine and the coordinator at query time. The communication cost is bounded, and the work load on each machine is balanced. Comprehensive experiments conducted on five real datasets demonstrate the efficiency and the scalability of our proposed methods.en
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en
dc.description.sponsorshipMOE (Min. of Education, S’pore)en
dc.language.isoenen
dc.rights© 2017 Association for Computing Machinery (ACM). This is the author created version of a work that has been peer reviewed and accepted for publication by Proceedings of the 2017 ACM International Conference on Management of Data (SIGMOD '17), Association for Computing Machinery (ACM). It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [https://doi.org/10.1145/3035918.3035920].en
dc.subjectPersonalized PageRanken
dc.subjectRandom walksen
dc.titleDistributed Algorithms on Exact Personalized PageRanken
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en
dc.contributor.conferenceProceedings of the 2017 ACM International Conference on Management of Data (SIGMOD '17)en
dc.contributor.researchRapid-Rich Object Search Laben
dc.identifier.doi10.1145/3035918.3035920en
dc.description.versionAccepted versionen
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Appears in Collections:IGS Conference Papers
SCSE Conference Papers
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