Efficient sampling from random web graph and its application

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dc.contributor.authorZhuang, Yan
dc.contributor.supervisorKing, Valerie
dc.date.accessioned2009-01-08T16:20:01Z
dc.date.available2009-01-08T16:20:01Z
dc.date.copyright2008en_US
dc.date.issued2009-01-08T16:20:01Z
dc.degree.departmentDept. of Computer Scienceen_US
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractThis thesis presents space-efficient algorithms to sample from random web graphs generated by two important stochastic graph models based on concept of copying: the linear copy model and the hostgraph model. The goal is to avoid constructing the entire random graph, and instead use an amount of space nearer to the desired (smaller) sample size. The efficiency of our algorithms is achieved by refraining from making unnecessary random decisions when constructing the sample. The construc- tion of a sample subgraph from a random graph with n nodes and k outgoing links on each node based on the linear copying model uses an expected O(klnn) words for each node in the sample subgraph. The construction of a sample subgraph from a random graph with n nodes based on the hostgraph model uses, for any small sample size, an expected n+o(n) words.en_US
dc.identifier.bibliographicCitationValerie King, Louis Lei Yu, and Yan Zhuang, Guanxi in the chinese web - a study of mutual linking, WWW, 2008, pp. 1161–1162.en_US
dc.identifier.urihttp://hdl.handle.net/1828/1327
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectweb graphen_US
dc.subjectsamplingen_US
dc.subjectgraph modelen_US
dc.subjectalgorithmen_US
dc.subject.lcshUVic Subject Index::Sciences and Engineering::Applied Sciences::Computer scienceen_US
dc.titleEfficient sampling from random web graph and its applicationen_US
dc.typeThesisen_US

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