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Clustering Methods for Network Adjacency Data

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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.
subject
Clustering algorithms
Networks analysis
contributor
Zhang, Teng (author)
Berenhaut, Kenneth S (committee chair)
Norris, James (committee member)
Erhardt, Robert (committee member)
date
2016-08-25T08:35:22Z (accessioned)
2016 (issued)
degree
Mathematics and Statistics (discipline)
2021-09-01 (liftdate)
embargo
2021-09-01 (terms)
identifier
http://hdl.handle.net/10339/62641 (uri)
language
en (iso)
publisher
Wake Forest University
title
Clustering Methods for Network Adjacency Data
type
Thesis

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