UNCOVERING LOCAL STRUCTURE IN DATA
Electronic Theses and Dissertations
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Item Details
- title
- UNCOVERING LOCAL STRUCTURE IN DATA
- author
- Melvin, Ryan Lee
- 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
- Berenhaut, Kenneth S (committee chair)
- Raynor, Sarah G (committee member)
- Salsbury, Freddie R (committee member)
- date
- 2018-05-24T08:36:08Z (accessioned)
- 2023-06-01T08:30:24Z (available)
- 2018 (issued)
- degree
- Mathematics and Statistics (discipline)
- embargo
- 2023-06-01 (terms)
- identifier
- http://hdl.handle.net/10339/90722 (uri)
- language
- en (iso)
- publisher
- Wake Forest University
- type
- Thesis