Date of Award
Spring 5-15-2024
Document Type
Thesis (Ph.D.)
Department or Program
Engineering Sciences
First Advisor
Peter Chin
Second Advisor
Vikrant Vaze
Third Advisor
Colin Meyer
Abstract
This thesis explores a variety of common graph theoretic problems from a machine learning perspective. The topics covered include fundamental network problems such as distance approximation, distance sensitivity, community detection, cross-network alignment, and graph embedding dimension reduction. These projects are unified by the theme of machine learning on graphs, graph embeddings, and representations of graphs.
Recommended Citation
Gunby-Mann, Allison, "Machine Learning for Graph Algorithms and Representations" (2024). Dartmouth College Ph.D Dissertations. 283.
https://digitalcommons.dartmouth.edu/dissertations/283
COinS