2026-09-19T11:11:39Zhttps://wakespace.lib.wfu.edu/server/oai/request

oai:null:10339/626412026-09-02T12:16:15Zcom_10339_14934col_10339_38132

Zhang, Teng 2016-08-25T08:35:22Z 2021-09-01T08:30:11Z 2016 https://wakespace.lib.wfu.edu/handle/10339/62641 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. en Wake Forest University Clustering algorithms Networks analysis Clustering Methods for Network Adjacency Data Thesis Berenhaut, Kenneth S Norris, James Erhardt, Robert Mathematics and Statistics 2021-09-01 open.access