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dc.contributor.advisorZhuang, Xinhuaeng
dc.contributor.authorSong, Boeng
dc.date.issued2018eng
dc.date.submitted2018 Springeng
dc.description.abstractThe enormous size and fast-evolving nature of World-Wide-Web has been demanding an even more efficient PageRank updating algorithm. Web evolution may involve two kinds: (1) link structure modification; (2) page insertion/deletion. When the web evolution is restricted to only link insertion/deletion, we demonstrate the benefit of using the previous PageRank to initialize the current PageRank computation, theoretically and experimentally. When page insertion/deletion occurs, how to effectively use the previous PageRank information to facilitate the current PageRank computation has long been a challenge. To tackle the general case, a so-called "virtual web" is introduced through adding the inserted nodes to the previous web along with some specific "in-home" link structure, where in-links from the previous web and out-links to the previous web are excluded. Through the virtual web, we are able to work out a virtual initialization, which can be efficiently used to calculate the current PageRank. The introduced virtual initialization is "unbiased", that assumes least under available knowledge. The virtual web is then integrated with the Power-Iteration and Gauss-Southwell method to solve the node insertion/deletion problem, which are named as Virtual Web Power-Iteration (VWPI) method and Virtual Web Gauss-Southwell (VWGS) method, respectively. Further, we proposed an optimized approach based on VWGS method for updating node insertions. The experiment result shows that the VWGS algorithm significantly outperformed the conventional PageRank computation based on the original model. On the dataset Twitter-2010 with 42M nodes and 1.5B edges, for a perturbation of 400k node and 14 million link insertions plus deletions at one time, our algorithm is about 20 times faster on number of iterations and 3 times faster on running-time in comparison to the Gauss-Southwell method starting from scratch. On the soc-LiveJournal dataset with up to a 20% node insertion, the optimized VWGS method received another 28% gain comparing to the original VWGS method. To compare with the prior work proposed by Ohsaka et al. in [32], our method is 1800x faster per link insertion/deletion on the Twitter-2010 dataset under similar experiment environment.eng
dc.description.bibrefIncludes bibliographic referenceseng
dc.format.extentix, 94 pages : illustrationeng]
dc.format.extentix, 94 pages : illustrationeng]
dc.identifier.urihttps://hdl.handle.net/10355/66110
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcollectionUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsOpenAccesseng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Licenseeng
dc.titleVirtual web for PageRank computingeng
dc.typeThesiseng
thesis.degree.disciplineComputer science (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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