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Comprehensive Exam Presentation - Janet Layne

April 22, 2022 @ 3:00 pm MDT

Janet Layne – Computer Science

Zoom Link

Title: Unsupervised Methods for Learning Structural Graph Representations


Node representation learning methods generate vectorial representations of the nodes in a network for use in standard machine learning models. These methods project nodes into a low-dimensional representation space while preserving information about relationships between them in the graph.
Approaches largely fall into one of two categories: those that capture information about connectivity between nodes, and those that capture a node’s structural information. For tasks where node structural role is important, connectivity-based methods show poor performance. Compared to connectivity-based methods, relatively few approaches exist that generate structural node representations. A review of the common structural methods will be presented to highlight the continued need for development of new approaches.


Edoardo Serra, Advisor
Francesca Spezzano
Marion Scheepers
Sole Pera
Michael Ekstrand, CompEE