{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/122154"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/122154","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Improving the accuracy of community detection methods using connectivity modifier","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","abstract_has_math":false,"creators":["Tabatabaee, Seyedeh Yasamin"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Warnow, Tandy"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Community Detection","Clustering","Connectivity","Leiden","Lfr Graphs"],"languages":["en","eng"],"rights":["Copyright 2023 Seyedeh Yasamin Tabatabaee"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/122154","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Warnow, Tandy"]},{"key":"dc:creator","label":"Author","values":["Tabatabaee, Seyedeh Yasamin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-12-04"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Community Detection","Clustering","Connectivity","Leiden","Lfr Graphs"]}]},{"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 Seyedeh Yasamin Tabatabaee"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/122154"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","The student, Seyedeh Yasamin Tabatabaee, accepted the attached license on 2023-11-29 at 10:04.","The student, Seyedeh Yasamin Tabatabaee, submitted this Thesis for approval on 2023-11-29 at 10:32.","This Thesis was approved for publication on 2023-12-04 at 13:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20056 on 2024-03-01 at 13:31:33","Community detection algorithms are commonly used to recover the community structure of complex networks. To evaluate the accuracy of these algorithms and compare them against each other, one would need to apply them to networks with known community structure. However, the community structure of real-world networks is usually not known, and hence synthetic networks with ground-truth communities are used for benchmarking these algorithms. The most widely adopted synthetic networks for the evaluation of community detection methods are the Lancichinetti-Fortunato-Radicchi (LFR) benchmark graphs. Here we develop a pipeline for creating LFR graphs that emulate the characteristics of given real-world networks and their clusterings. While our study shows that these LFR graphs almost perfectly match some characteristics of the real-world networks they attempt to emulate, there are striking differences among their other properties. We also evaluate the recently introduced Connectivity Modifier (CM) algorithm, a meta-method for ensuring well-connectedness of clusters outputted by community detection methods, on these empirical networks and LFR graphs. Our results show that while CM reduces node coverage, it improves the accuracy of Leiden algorithm optimizing modularity or the Constant Potts model (CPM) in many model conditions."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Improving the accuracy of community detection methods using connectivity modifier"]}]}],"canonical_facts":{"dc:contributor":["Warnow, Tandy"],"dc:creator":["Tabatabaee, Seyedeh Yasamin"],"dc:date":["2023-12","2023-12-04"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","The student, Seyedeh Yasamin Tabatabaee, accepted the attached license on 2023-11-29 at 10:04.","The student, Seyedeh Yasamin Tabatabaee, submitted this Thesis for approval on 2023-11-29 at 10:32.","This Thesis was approved for publication on 2023-12-04 at 13:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20056 on 2024-03-01 at 13:31:33","Community detection algorithms are commonly used to recover the community structure of complex networks. To evaluate the accuracy of these algorithms and compare them against each other, one would need to apply them to networks with known community structure. However, the community structure of real-world networks is usually not known, and hence synthetic networks with ground-truth communities are used for benchmarking these algorithms. The most widely adopted synthetic networks for the evaluation of community detection methods are the Lancichinetti-Fortunato-Radicchi (LFR) benchmark graphs. Here we develop a pipeline for creating LFR graphs that emulate the characteristics of given real-world networks and their clusterings. While our study shows that these LFR graphs almost perfectly match some characteristics of the real-world networks they attempt to emulate, there are striking differences among their other properties. We also evaluate the recently introduced Connectivity Modifier (CM) algorithm, a meta-method for ensuring well-connectedness of clusters outputted by community detection methods, on these empirical networks and LFR graphs. Our results show that while CM reduces node coverage, it improves the accuracy of Leiden algorithm optimizing modularity or the Constant Potts model (CPM) in many model conditions."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/122154"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Seyedeh Yasamin Tabatabaee"],"dc:subject":["Community Detection","Clustering","Connectivity","Leiden","Lfr Graphs"],"dc:title":["Improving the accuracy of community detection methods using connectivity modifier"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}