Item Details

title

Clustering Methods for Network Adjacency Data

author

Zhang, Teng

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

Berenhaut, Kenneth S (committee chair)

Norris, James (committee member)

Erhardt, Robert (committee member)

date

2016-08-25T08:35:22Z (accessioned)

2021-09-01T08:30:11Z (available)

2016 (issued)

degree

Mathematics and Statistics (discipline)

embargo

2021-09-01 (terms)

identifier

http://hdl.handle.net/10339/62641 (uri)

language

en (iso)

publisher

Wake Forest University

type

Thesis