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Abstract: This thesis will provide a survey 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.

Thesis Committee: Peter Chin (chair), Vikrant Vaze, Colin Meyer, Wayne Snyder (Boston University)