{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/122228"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/122228","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Identifiability and estimation of mixed membership stochastic blockmodels","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01","abstract_has_math":false,"creators":["He, Shishuang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":["Liang, Feng","Yang, Yun","Chen, Yuguo","Liu, Jingbo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Mixed Membership Stochastic Blockmodels","Identifiability","Volume Minimization","Sufficiently Scattered","Volume-penalized Integrated Likelihood"],"languages":["en","eng"],"rights":["Copyright 2023 Shishuang He"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/122228","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Liang, Feng","Yang, Yun","Chen, Yuguo","Liu, Jingbo"]},{"key":"dc:creator","label":"Author","values":["He, Shishuang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-11-30"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Mixed Membership Stochastic Blockmodels","Identifiability","Volume Minimization","Sufficiently Scattered","Volume-penalized Integrated Likelihood"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Shishuang He"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/122228"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Identifiability and estimation of mixed membership stochastic blockmodels"]}]}],"canonical_facts":{"dc:contributor":["Liang, Feng","Yang, Yun","Chen, Yuguo","Liu, Jingbo"],"dc:creator":["He, Shishuang"],"dc:date":["2023-12","2023-11-30"],"dc:description":["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","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/122228"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Shishuang He"],"dc:subject":["Mixed Membership Stochastic Blockmodels","Identifiability","Volume Minimization","Sufficiently Scattered","Volume-penalized Integrated Likelihood"],"dc:title":["Identifiability and estimation of mixed membership stochastic blockmodels"],"dc:type":["text"],"thesis:degree_discipline":["Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}