{"id":{"repo_id":"wfu","oai_identifier":"oai:wakespace.lib.wfu.edu:10339/62641"},"canonical_url":"https://search.dev.ndltd.org/etd/wfu/oai:wakespace.lib.wfu.edu:10339/62641","repository":{"repo_id":"wfu","name":"Wake Forest University","base_url":"https://wakespace.lib.wfu.edu/oai/request"},"display":{"title":"Clustering Methods for Network Adjacency Data","abstract":"Clustering analysis aims to detect the topological community-structure of networks (connected graphs with n vertices and m edges), and studies inherent relations behind partitions. In this thesis, we consider far-reaching model-free clustering algorithms including Girvan and Newman’s edge betweenness algorithm, Zhou’s dissimilarity algorithm, the Walktrap algorithm, the leading eigenvector algorithm, the fast-greedy algorithm and the Louvain method, in relation to each other. We also introduce a unified and natural approach, based on a newly defined dissimilarity, to clustering subsets of vertices, which are considered to be active (occupied, selected) within a network. The informativeness and effectiveness of these algorithms are considered in relation to well-known real-world test-case datasets (with reasonable ground truths) including Zachary’s karate network, the U.S. football network, a dolphins social network, a macaque brain network, a cat cortex network, and a political books network.","abstract_html":"Clustering analysis aims to detect the topological community-structure of networks (connected graphs with n vertices and m edges), and studies inherent relations behind partitions. In this thesis, we consider far-reaching model-free clustering algorithms including Girvan and Newman’s edge betweenness algorithm, Zhou’s dissimilarity algorithm, the Walktrap algorithm, the leading eigenvector algorithm, the fast-greedy algorithm and the Louvain method, in relation to each other. We also introduce a unified and natural approach, based on a newly defined dissimilarity, to clustering subsets of vertices, which are considered to be active (occupied, selected) within a network. The informativeness and effectiveness of these algorithms are considered in relation to well-known real-world test-case datasets (with reasonable ground truths) including Zachary’s karate network, the U.S. football network, a dolphins social network, a macaque brain network, a cat cortex network, and a political books network.","abstract_has_math":false,"creators":["Zhang, Teng"],"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":2016,"date_issued":"2016","date_published":"2016","updated_at":"2026-07-27T22:02:05Z","subjects":["Clustering algorithms"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10339/62641","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Zhang, Teng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-08-25T08:35:22Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-09-01T08:30:11Z"]},{"key":"dc:date.issued","label":"Date","values":["2016"]},{"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 algorithms"]}]},{"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/10339/62641"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Clustering analysis aims to detect the topological community-structure of networks (connected graphs with n vertices and m edges), and studies inherent relations behind partitions. In this thesis, we consider far-reaching model-free clustering algorithms including Girvan and Newman’s edge betweenness algorithm, Zhou’s dissimilarity algorithm, the Walktrap algorithm, the leading eigenvector algorithm, the fast-greedy algorithm and the Louvain method, in relation to each other. We also introduce a unified and natural approach, based on a newly defined dissimilarity, to clustering subsets of vertices, which are considered to be active (occupied, selected) within a network. The informativeness and effectiveness of these algorithms are considered in relation to well-known real-world test-case datasets (with reasonable ground truths) including Zachary’s karate network, the U.S. football network, a dolphins social network, a macaque brain network, a cat cortex network, and a political books network."]},{"key":"dc:title","label":"Title","values":["Clustering Methods for Network Adjacency Data"]}]}],"canonical_facts":{"dc:creator":["Zhang, Teng"],"dc:date.accessioned":["2016-08-25T08:35:22Z"],"dc:date.available":["2021-09-01T08:30:11Z"],"dc:date.issued":["2016"],"dc:description.abstract":["Clustering analysis aims to detect the topological community-structure of networks (connected graphs with n vertices and m edges), and studies inherent relations behind partitions. In this thesis, we consider far-reaching model-free clustering algorithms including Girvan and Newman’s edge betweenness algorithm, Zhou’s dissimilarity algorithm, the Walktrap algorithm, the leading eigenvector algorithm, the fast-greedy algorithm and the Louvain method, in relation to each other. We also introduce a unified and natural approach, based on a newly defined dissimilarity, to clustering subsets of vertices, which are considered to be active (occupied, selected) within a network. The informativeness and effectiveness of these algorithms are considered in relation to well-known real-world test-case datasets (with reasonable ground truths) including Zachary’s karate network, the U.S. football network, a dolphins social network, a macaque brain network, a cat cortex network, and a political books network."],"dc:identifier.uri":["http://hdl.handle.net/10339/62641"],"dc:language.iso":["en"],"dc:publisher":["Wake Forest University"],"dc:subject":["Clustering algorithms"],"dc:title":["Clustering Methods for Network Adjacency Data"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T22:02:05Z"}