Home WakeSpace Scholarship › Electronic Theses and Dissertations

UNCOVERING LOCAL STRUCTURE IN DATA

Electronic Theses and Dissertations

Item Files

Item Details

abstract
In this thesis, we explore in depth a unified measure of community-based proximity between network nodes, relative to a given subset of interest. In addition, we develop a novel measure of local centrality for data and apply the method in the context of networks and elsewhere.
subject
Centrality
Clustering
Community detection
Depth
Networks
Random walks
contributor
Melvin, Ryan Lee (author)
Berenhaut, Kenneth S (committee chair)
Raynor, Sarah G (committee member)
Salsbury, Freddie R (committee member)
date
2018-05-24T08:36:08Z (accessioned)
2018 (issued)
degree
Mathematics and Statistics (discipline)
2023-06-01 (liftdate)
embargo
2023-06-01 (terms)
identifier
http://hdl.handle.net/10339/90722 (uri)
language
en (iso)
publisher
Wake Forest University
title
UNCOVERING LOCAL STRUCTURE IN DATA
type
Thesis

Usage Statistics