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Doctor of Philosophy (PhD)
Year Degree Awarded
Month Degree Awarded
Artificial Intelligence and Robotics | Information Security | Physics | Theory and Algorithms
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.
Atwood, James, "Problems in Graph-Structured Modeling and Learning" (2017). Doctoral Dissertations. 929.