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

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dc.contributor.author Wei, Ran
dc.date.accessioned 2017-08-18T18:25:57Z
dc.date.available 2017-08-18T18:25:57Z
dc.date.copyright 2017 en_US
dc.date.issued 2017-08-18
dc.identifier.uri http://hdl.handle.net/1828/8432
dc.description.abstract k-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.language English eng
dc.language.iso en en_US
dc.rights Available to the World Wide Web en_US
dc.subject k-core en_US
dc.subject influential community en_US
dc.subject large network en_US
dc.title Efficient algorithms for discovering importance-based communities in large web-scale networks en_US
dc.type Thesis en_US
dc.contributor.supervisor Thomo, Alex
dc.degree.department Department of Computer Science en_US
dc.degree.level Master of Science M.Sc. en_US
dc.description.scholarlevel Graduate en_US


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