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Inference on Graphs: From Probabilit...

Li, Xiang.

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Inference on Graphs: From Probability Methods to Deep Neural Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Inference on Graphs: From Probability Methods to Deep Neural Networks./
Author:	Li, Xiang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	72 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10281975
ISBN:	9780355034097

Inference on Graphs: From Probability Methods to Deep Neural Networks.
Li, Xiang.

Inference on Graphs: From Probability Methods to Deep Neural Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 72 p.

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2017.

Graphs are a rich and fundamental object of study, of interest from both theoretical and applied points of view. This thesis is in two parts and gives a treatment of graphs from two differing points of view, with the goal of doing inference on graphs. The first is a mathematical approach. We create a formal framework to investigate the quality of inference on graphs given partial observations. The proofs we give apply to all graphs without assumptions. In the second part of this thesis, we take on the problem of clustering with the aid of deep neural networks and apply it to the problem of community detection. The results are competitive with the state of the art, even at the information theoretic threshold of recovery of community labels in the stochastic blockmodel.

ISBN: 9780355034097Subjects--Topical Terms:

517247
Statistics.

Inference on Graphs: From Probability Methods to Deep Neural Networks.
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300 $a 72 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: David Aldous.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2017.
520 $a Graphs are a rich and fundamental object of study, of interest from both theoretical and applied points of view. This thesis is in two parts and gives a treatment of graphs from two differing points of view, with the goal of doing inference on graphs. The first is a mathematical approach. We create a formal framework to investigate the quality of inference on graphs given partial observations. The proofs we give apply to all graphs without assumptions. In the second part of this thesis, we take on the problem of clustering with the aid of deep neural networks and apply it to the problem of community detection. The results are competitive with the state of the art, even at the information theoretic threshold of recovery of community labels in the stochastic blockmodel.
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