Efficient algorithms for discovering importance-based communities in large web-scale networks

dc.contributor.authorWei, Ran
dc.contributor.supervisorThomo, Alex
dc.date.accessioned2017-08-18T18:25:57Z
dc.date.available2017-08-18T18:25:57Z
dc.date.copyright2017en_US
dc.date.issued2017-08-18
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractk-core is a notion capturing the cohesiveness of a subgraph in a social network graph. Most of the current research work only consider pure network graphs and neglect an important property of the nodes: influence. Li, Qin, Yu, and Mao introduced a novel community model called k-influential community which is based on the concept of k-core enhanced with node influence values. In this model, we are interested not only in subgraphs that are well-connected but also have a high lower-bound on their influence. More precisely, we are interested in finding top r (with respect to influence), k-core communities. We present novel approaches that provide an impressive scalability in solving the problem for graphs of billions of edges using only a consumer-grade machine.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/8432
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectk-coreen_US
dc.subjectinfluential communityen_US
dc.subjectlarge networken_US
dc.titleEfficient algorithms for discovering importance-based communities in large web-scale networksen_US
dc.typeThesisen_US

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