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"Machine Learning for Graph Algorithms and Representations"
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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), Wayne Snyder (from BU), Colin Meyer, and Vikrant Vaze
Events are free and open to the public unless otherwise noted.