Spectral analysis in bipartite biregular graphs and community detection

dc.contributor.advisorDumitriu, Ioana
dc.contributor.advisorHoffman, Christopher
dc.contributor.authorBrito, Gerandy
dc.date.accessioned2017-10-26T20:51:45Z
dc.date.available2017-10-26T20:51:45Z
dc.date.issued2017-10-26
dc.date.submitted2017-08
dc.descriptionThesis (Ph.D.)--University of Washington, 2017-08
dc.description.abstractThis thesis concerns to spectral gap of random regular graphs and consists of two main con- tributions. First, we prove that almost all bipartite biregular graphs are almost Ramanujan by providing a tight upper bound for the non trivial eigenvalues of its adjacency operator, proving Alon's Conjecture for this family of graphs. Secondly, we use a spectral algorithm to recover hidden communities in a random network model we call regular stochastic block model. We rely on a technique introduced recently by Massoullie, which we develop here for random regular graphs.
dc.embargo.termsOpen Access
dc.format.mimetypeapplication/pdf
dc.identifier.otherBrito_washington_0250E_17725.pdf
dc.identifier.urihttp://hdl.handle.net/1773/40636
dc.language.isoen_US
dc.rightsCC BY-NC
dc.subjectcommunity detection
dc.subjectregular graphs
dc.subjectspectral analysis
dc.subjectspectral gap
dc.subjectMathematics
dc.subject.otherMathematics
dc.titleSpectral analysis in bipartite biregular graphs and community detection
dc.typeThesis

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