Dissertation/Thesis Abstract

Community Detection: Fundamental Limits, Methodology, and Variational Inference
by Zhang, Ye, Ph.D., Yale University, 2018, 94; 10957347
Abstract (Summary)

Network analysis has become one of the most active research areas over the past few years. A core problem in network analysis is community detection. In this thesis, we investigate it under Stochastic Block Model and Degree-corrected Block Model from three different perspectives: 1) the minimax rates of community detection problem, 2) rate-optimal and computationally feasible algorithms, and 3) computational and theoretical guarantees of variational inference for community detection.

Indexing (document details)
Advisor: Zhou, Harrison H.
Commitee:
School: Yale University
School Location: United States -- Connecticut
Source: DAI-B 79/12(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Statistics
Keywords: Community Detection, Minimax, Network Science, Spectral Clustering, Stochastic Block Model, Variational Inference
Publication Number: 10957347
ISBN: 978-0-438-27392-4
Copyright © 2021 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest