{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129181"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129181","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Synthetic network generation with realistic cluster connectivity","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Anne, Lahari"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Chacko, George"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-03-24","date_published":"2025-03-24","updated_at":"2026-07-22T22:25:04Z","subjects":["synthetic networks","community detection"],"languages":["en","eng"],"rights":["Copyright 2025 Lahari Anne"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129181","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chacko, George"]},{"key":"dc:creator","label":"Author","values":["Anne, Lahari"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-03-24","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["synthetic networks","community detection"]}]},{"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 2025 Lahari Anne"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129181"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Lahari Anne, accepted the attached license on 2025-03-20 at 21:48.","The student, Lahari Anne, submitted this Thesis for approval on 2025-03-20 at 22:07.","This Thesis was approved for publication on 2025-03-24 at 10:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21686 on 2025-10-19 at 18:09:11","Evaluating the effectiveness of community detection methods is challenging due to the scarcity of real-world networks with known ground-truth communities. To address this, synthetic networks with predefined communities serve as valuable benchmarks. Among various synthetic network generators, Stochastic Block Models (SBMs) are widely used as they can approximate real-world network properties when provided with input parameters derived from real-world networks. However, SBMs often generate disconnected clusters, even when the input clustering exhibits fully connected communities, leading to structural inconsistencies that may affect the accuracy of performance evaluations for community detection algorithms. In this study, we introduce the REalistic Cluster Connectivity Simulator (RECCS), a post-processing framework designed to enhance SBM-generated networks by improving their fit to the cluster edge connectivity observed in real-world networks. RECCS modifies the synthetic network structure to better capture intra-cluster connectivity while preserving other essential network and clustering properties. This approach is evaluated on large-scale real-world networks containing up to 13.9 million nodes. The results show that RECCS generally improves the alignment of synthetic networks with empirical cluster connectivity, with some minimal trade-offs observed in other network properties. These findings suggest that RECCS offers a useful solution for generating synthetic benchmarks that more closely reflect real-world community structures, highlighting both its potential and limitations for community detection research."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Synthetic network generation with realistic cluster connectivity"]}]}],"canonical_facts":{"dc:contributor":["Chacko, George"],"dc:creator":["Anne, Lahari"],"dc:date":["2025-03-24","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Lahari Anne, accepted the attached license on 2025-03-20 at 21:48.","The student, Lahari Anne, submitted this Thesis for approval on 2025-03-20 at 22:07.","This Thesis was approved for publication on 2025-03-24 at 10:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21686 on 2025-10-19 at 18:09:11","Evaluating the effectiveness of community detection methods is challenging due to the scarcity of real-world networks with known ground-truth communities. To address this, synthetic networks with predefined communities serve as valuable benchmarks. Among various synthetic network generators, Stochastic Block Models (SBMs) are widely used as they can approximate real-world network properties when provided with input parameters derived from real-world networks. However, SBMs often generate disconnected clusters, even when the input clustering exhibits fully connected communities, leading to structural inconsistencies that may affect the accuracy of performance evaluations for community detection algorithms. In this study, we introduce the REalistic Cluster Connectivity Simulator (RECCS), a post-processing framework designed to enhance SBM-generated networks by improving their fit to the cluster edge connectivity observed in real-world networks. RECCS modifies the synthetic network structure to better capture intra-cluster connectivity while preserving other essential network and clustering properties. This approach is evaluated on large-scale real-world networks containing up to 13.9 million nodes. The results show that RECCS generally improves the alignment of synthetic networks with empirical cluster connectivity, with some minimal trade-offs observed in other network properties. These findings suggest that RECCS offers a useful solution for generating synthetic benchmarks that more closely reflect real-world community structures, highlighting both its potential and limitations for community detection research."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129181"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Lahari Anne"],"dc:subject":["synthetic networks","community detection"],"dc:title":["Synthetic network generation with realistic cluster connectivity"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}