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dc.contributor.authorAndrews, Juneen_US
dc.date.accessioned2013-01-31T19:44:03Z
dc.date.available2017-12-20T07:00:23Z
dc.date.issued2012-08-20en_US
dc.identifier.otherbibid: 7959769
dc.identifier.urihttps://hdl.handle.net/1813/31046
dc.description.abstractGraphs are used to represent various large and complex networks in scientific applications. In order to understand the structure of these graphs, it is useful to treat a set of nodes with similar characteristics as one community and analyze the community's behavior as a whole. Finding all such communities within the graph is the object of community detection. In our research, we compare dozens of existing community detection methods and develop a new class of algorithms for finding communities.en_US
dc.language.isoen_USen_US
dc.subjectcommunity detectionen_US
dc.subjectsocial networksen_US
dc.titleCommunity Detection In Large Networksen_US
dc.typedissertation or thesisen_US
thesis.degree.disciplineApplied Mathematics
thesis.degree.grantorCornell Universityen_US
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Applied Mathematics
dc.contributor.chairHopcroft, John Een_US
dc.contributor.committeeMemberKleinberg, Jon Men_US
dc.contributor.committeeMemberStrogatz, Steven Hen_US


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