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.|
|School Location:||United States -- Connecticut|
|Source:||DAI-B 79/12(E), Dissertation Abstracts International|
|Keywords:||Community Detection, Minimax, Network Science, Spectral Clustering, Stochastic Block Model, Variational Inference|
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