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dc.contributor.authorCevahir, A.en_US
dc.contributor.authorAykanat, C.en_US
dc.contributor.authorTurk, A.en_US
dc.contributor.authorCambazoglu, B.B.en_US
dc.date.accessioned2016-02-08T11:43:14Z
dc.date.available2016-02-08T11:43:14Z
dc.date.issued2007en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/27060
dc.description.abstractA power method formulation, which efficiently handles the problem of dangling pages, is investigated for parallelization of PageRank computation. Hypergraph-partitioning-based sparse matrix partitioning methods can be successfully used for efficient parallelization. However, the preprocessing overhead due to hypergraph partitioning, which must be repeated often due to the evolving nature of the Web, is quite significant compared to the duration of the PageRank computation. To alleviate this problem, we utilize the information that sites form a natural clustering on pages to propose a site-based hypergraph-partitioning technique, which does not degrade the quality of the parallelization. We also propose an efficient parallelization scheme for matrix-vector multiplies in order to avoid possible communication due to the pages without in-links. Experimental results on realistic datasets validate the effectiveness of the proposed models. © Springer-Verlag Berlin Heidelberg 2007.en_US
dc.language.isoEnglishen_US
dc.source.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.subjectClustering algorithmsen_US
dc.subjectComputation theoryen_US
dc.subjectData structuresen_US
dc.subjectParallel processing systemsen_US
dc.subjectProblem solvingen_US
dc.subjectPageRank computationen_US
dc.subjectParallelizationen_US
dc.subjectWebsitesen_US
dc.titleA web-site-based partitioning technique for reducing preprocessing overhead of parallel PageRank computationen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineering
dc.citation.spage908en_US
dc.citation.epage918en_US
dc.citation.volumeNumber4699 LNCSen_US


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