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Spectral Analysis in Bipartite Bireg...
~
University of Washington.
Spectral Analysis in Bipartite Biregular Graphs and Community Detection.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Spectral Analysis in Bipartite Biregular Graphs and Community Detection./
作者:
Brito, Gerandy.
面頁冊數:
1 online resource (82 pages)
附註:
Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:
Dissertation Abstracts International79-04B(E).
標題:
Mathematics. -
電子資源:
click for full text (PQDT)
ISBN:
9780355355659
Spectral Analysis in Bipartite Biregular Graphs and Community Detection.
Brito, Gerandy.
Spectral Analysis in Bipartite Biregular Graphs and Community Detection.
- 1 online resource (82 pages)
Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Thesis (Ph.D.)
Includes bibliographical references
This thesis concerns to spectral gap of random regular graphs and consists of two main contributions. 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.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9780355355659Subjects--Topical Terms:
527692
Mathematics.
Index Terms--Genre/Form:
554714
Electronic books.
Spectral Analysis in Bipartite Biregular Graphs and Community Detection.
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Advisers: Ioana Dumitriu; Christopher Hoffman.
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University of Washington
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This thesis concerns to spectral gap of random regular graphs and consists of two main contributions. 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.
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Ann Arbor, Mich. :
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ProQuest,
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2018
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click for full text (PQDT)
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