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Author ORCID Identifier
https://orcid.org/0000-0003-1412-7195
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Computer Science
Year Degree Awarded
2021
Month Degree Awarded
February
First Advisor
Don Towsley
Second Advisor
Ben Marlin
Third Advisor
Andrew McCallum
Fourth Advisor
Weibo Gong
Subject Categories
Artificial Intelligence and Robotics
Abstract
The information age has led to an explosion in the size and availability of data. This data often exhibits graph-structure that is either explicitly defined, as in the web of a social network, or is implicitly defined and can be determined by measuring similarity between objects. Utilizing this graph-structure allows for the design of machine learning algorithms that reflect not only the attributes of individual objects but their relationships to every other object in the domain as well. This thesis investigates three machine learning problems and proposes novel methods that leverage the graph-structure inherent in the tasks. Quantum walk neural networks are classical neural nets that use quantum random walks for classifying and regressing on graphs. Asymmetric directed node embeddings are another neural network architecture designed to embed the nodes of a directed graph into a vector space. Filtered manifold alignment is a novel two-step approach to domain adaptation.
DOI
https://doi.org/10.7275/20154270
Recommended Citation
Dernbach, Stefan, "Utilizing Graph Structure for Machine Learning" (2021). Doctoral Dissertations. 2102.
https://doi.org/10.7275/20154270
https://scholarworks.umass.edu/dissertations_2/2102
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.