{"id":{"repo_id":"wfu","oai_identifier":"oai:wakespace.lib.wfu.edu:10339/82258"},"canonical_url":"https://search.dev.ndltd.org/etd/wfu/oai:wakespace.lib.wfu.edu:10339/82258","repository":{"repo_id":"wfu","name":"Wake Forest University","base_url":"https://wakespace.lib.wfu.edu/oai/request"},"display":{"title":"A New Look at Clustering Coefficients with Generalization to Weighted and Multi-Faction Networks","abstract":"In this thesis, we propose a new method for studying local and global clustering in networks employing random walk pairs. The method is intuitive and directly generalizes standard local and global clustering coefficients to weighted networks and networks containing nodes of multiple types. In the case of two-mode networks, the values obtained for commonly considered social networks are in sharp contrast to those obtained by previous methods, and provide a different viewpoint for clustering. The approach is also applicable in questions related to the general study of segregation and homophily. Applications to existent data sets are considered.","abstract_html":"In this thesis, we propose a new method for studying local and global clustering in networks employing random walk pairs. The method is intuitive and directly generalizes standard local and global clustering coefficients to weighted networks and networks containing nodes of multiple types. In the case of two-mode networks, the values obtained for commonly considered social networks are in sharp contrast to those obtained by previous methods, and provide a different viewpoint for clustering. The approach is also applicable in questions related to the general study of segregation and homophily. Applications to existent data sets are considered.","abstract_has_math":false,"creators":["Kotsonis, Rebecca"],"institution":"Wake Forest University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-27T22:02:11Z","subjects":["Clustering coefficient"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10339/82258","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Kotsonis, Rebecca"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-06-15T08:36:22Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-06-14T08:30:12Z"]},{"key":"dc:date.issued","label":"Date","values":["2017"]},{"key":"dc:publisher","label":"Institution","values":["Wake Forest University"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Clustering coefficient"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10339/82258"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis, we propose a new method for studying local and global clustering in networks employing random walk pairs. The method is intuitive and directly generalizes standard local and global clustering coefficients to weighted networks and networks containing nodes of multiple types. In the case of two-mode networks, the values obtained for commonly considered social networks are in sharp contrast to those obtained by previous methods, and provide a different viewpoint for clustering. The approach is also applicable in questions related to the general study of segregation and homophily. Applications to existent data sets are considered."]},{"key":"dc:title","label":"Title","values":["A New Look at Clustering Coefficients with Generalization to Weighted and Multi-Faction Networks"]}]}],"canonical_facts":{"dc:creator":["Kotsonis, Rebecca"],"dc:date.accessioned":["2017-06-15T08:36:22Z"],"dc:date.available":["2019-06-14T08:30:12Z"],"dc:date.issued":["2017"],"dc:description.abstract":["In this thesis, we propose a new method for studying local and global clustering in networks employing random walk pairs. The method is intuitive and directly generalizes standard local and global clustering coefficients to weighted networks and networks containing nodes of multiple types. In the case of two-mode networks, the values obtained for commonly considered social networks are in sharp contrast to those obtained by previous methods, and provide a different viewpoint for clustering. The approach is also applicable in questions related to the general study of segregation and homophily. Applications to existent data sets are considered."],"dc:identifier.uri":["http://hdl.handle.net/10339/82258"],"dc:language.iso":["en"],"dc:publisher":["Wake Forest University"],"dc:subject":["Clustering coefficient"],"dc:title":["A New Look at Clustering Coefficients with Generalization to Weighted and Multi-Faction Networks"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T22:02:11Z"}