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Author ORCID Identifier
N/A
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Computer Science
Year Degree Awarded
2017
Month Degree Awarded
May
First Advisor
Don Towsley
Second Advisor
Benjamin Marlin
Third Advisor
Gerome Miklau
Fourth Advisor
Weibo Gong
Subject Categories
Artificial Intelligence and Robotics | Information Security | Physics | Theory and Algorithms
Abstract
This thesis investigates three problems in graph-structured modeling and learning. We first present a method for efficiently generating large instances from nonlinear preferential attachment models of network structure. This is followed by a description of diffusion-convolutional neural networks, a new model for graph-structured data which is able to outperform probabilistic relational models and kernel-on-graph methods at node classification tasks. We conclude with an optimal privacy-protection method for users of online services that remains effective when users have poor knowledge of an adversary's behavior.
DOI
https://doi.org/10.7275/10010449.0
Recommended Citation
Atwood, James, "Problems in Graph-Structured Modeling and Learning" (2017). Doctoral Dissertations. 929.
https://doi.org/10.7275/10010449.0
https://scholarworks.umass.edu/dissertations_2/929
Included in
Artificial Intelligence and Robotics Commons, Information Security Commons, Physics Commons, Theory and Algorithms Commons