2026-09-20T19:22:15Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/122228 2024-03-02 col_2142_5131 col_2142_17362 com_2142_5130 com_2142_17361 com_2142_8903

Liang, Feng Yang, Yun Liang, Feng Yang, Yun Chen, Yuguo Liu, Jingbo 2023-12 application/pdf en text Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01 The student, Shishuang He, accepted the attached license on 2023-11-20 at 02:03. The student, Shishuang He, submitted this Dissertation for approval on 2023-11-20 at 02:18. This Dissertation was approved for publication on 2023-11-30 at 10:47. DSpace SAF Submission Ingestion Package generated from Vireo submission #19957 on 2024-03-01 at 13:49:14 Identifiability and estimation of mixed membership stochastic blockmodels He, Shishuang Mixed Membership Stochastic Blockmodels Identifiability Volume Minimization Sufficiently Scattered Volume-penalized Integrated Likelihood 2023-11-30 The Mixed Membership Stochastic Blockmodel (MMSB) is a widely used method for detecting overlapping communities in large network data. However, MMSB is known to be unidentifiable, which presents a challenge for practical use. Previous approaches to MMSB identifiability rely on pure nodes, or nodes belonging to only one community, which is often too restrictive for real-world applications. In this paper, we propose a new, less restrictive set of identifiability conditions for MMSB and introduce an estimator based on a specific integrated likelihood with a penalty for volume. Our proposed estimator is demonstrated to have desirable asymptotic properties and can be efficiently computed using an MCMC-EM algorithm. We illustrate the benefits of our method through simulation studies and real data applications. Text eng https://hdl.handle.net/2142/122228 Copyright 2023 Shishuang He Ph.D. Dissertation Statistics University of Illinois at Urbana-Champaign Statistics