{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127150"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127150","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A framework for similarity-based clustering of biomedical citation graphs","abstract":"DSpace SAF Submission Ingestion Package generated from Vireo submission #21216 on 2025-03-28 at 14:25:03","abstract_html":"DSpace SAF Submission Ingestion Package generated from Vireo submission #21216 on 2025-03-28 at 14:25:03","abstract_has_math":false,"creators":["Mohasel Arjomandi, Hossein"],"institution":"University of Illinois at 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":2024,"date_issued":"2024-09-13","date_published":"2024-09-13","updated_at":"2026-07-22T22:25:03Z","subjects":["Clustering","Scientometrics","Community Detection","Biomedical Documents"],"languages":["en","eng"],"rights":["Copyright 2024 Hossein Mohasel Arjomandi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127150","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":["Mohasel Arjomandi, Hossein"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-09-13","2024-12"]},{"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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Clustering","Scientometrics","Community Detection","Biomedical Documents"]}]},{"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 2024 Hossein Mohasel Arjomandi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127150"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["DSpace SAF Submission Ingestion Package generated from Vireo submission #21216 on 2025-03-28 at 14:25:03","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Hossein Mohasel Arjomandi, accepted the attached license on 2024-09-06 at 15:00.","The student, Hossein Mohasel Arjomandi, submitted this Thesis for approval on 2024-09-06 at 15:13.","This Thesis was approved for publication on 2024-09-13 at 10:50.","In many scientific fields, identifying groups of similar items within complex systems is crucial, often referred to as community detection in networks. This research focuses on community detection in citation graphs of PubMed documents. While existing methods have explored clustering citation graphs, they often neglect article metadata or rely on non-scalable solutions. In this thesis, I propose a framework for community detection in any citation graph within the PubMed database, utilizing both article metadata and citation graph structure. The core of this framework is a similarity metric that measures document relevance by incorporating metadata and citation importance weights. Scalability is achieved through a parallel framework for data gathering and feature construction, demonstrated by clustering a 7.5 million node graph. The approach is flexible, allowing users to adjust feature importance and content. The effectiveness of this method was evaluated using the Leiden CPM clustering algorithm across various feature combinations to assess their impact on cluster quality. Additionally, the approach’s potential for application beyond citation graphs, such as in social networks, is discussed. In summary, the developed similarity metric enhances community detection by integrating both topology and metadata, providing a foundation for future research in this area."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A framework for similarity-based clustering of biomedical citation graphs"]}]}],"canonical_facts":{"dc:contributor":["Chacko, George"],"dc:creator":["Mohasel Arjomandi, Hossein"],"dc:date":["2024-09-13","2024-12"],"dc:description":["DSpace SAF Submission Ingestion Package generated from Vireo submission #21216 on 2025-03-28 at 14:25:03","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Hossein Mohasel Arjomandi, accepted the attached license on 2024-09-06 at 15:00.","The student, Hossein Mohasel Arjomandi, submitted this Thesis for approval on 2024-09-06 at 15:13.","This Thesis was approved for publication on 2024-09-13 at 10:50.","In many scientific fields, identifying groups of similar items within complex systems is crucial, often referred to as community detection in networks. This research focuses on community detection in citation graphs of PubMed documents. While existing methods have explored clustering citation graphs, they often neglect article metadata or rely on non-scalable solutions. In this thesis, I propose a framework for community detection in any citation graph within the PubMed database, utilizing both article metadata and citation graph structure. The core of this framework is a similarity metric that measures document relevance by incorporating metadata and citation importance weights. Scalability is achieved through a parallel framework for data gathering and feature construction, demonstrated by clustering a 7.5 million node graph. The approach is flexible, allowing users to adjust feature importance and content. The effectiveness of this method was evaluated using the Leiden CPM clustering algorithm across various feature combinations to assess their impact on cluster quality. Additionally, the approach’s potential for application beyond citation graphs, such as in social networks, is discussed. In summary, the developed similarity metric enhances community detection by integrating both topology and metadata, providing a foundation for future research in this area."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127150"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Hossein Mohasel Arjomandi"],"dc:subject":["Clustering","Scientometrics","Community Detection","Biomedical Documents"],"dc:title":["A framework for similarity-based clustering of biomedical citation graphs"],"dc:type":["text","Thesis"],"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":"2026-07-22T22:25:03Z"}