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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.
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 |