Introduction to Graph Machine Learning

This hybrid workshop provides graduate students with the necessary skills for understanding and applying graph machine learning techniques. Among the covered topics are the fundamentals of graph theory, practical applications of graph neural networks, and advanced methods for graph-based data analysis. 

We meet in the Weaver Science and Engineering Library in Rm 212. You can also join us via Zoom at https://arizona.zoom.us/j/86423223879.

Data/Topic/YouTube Link

   04/01/24: Part 1: Why Graph ML and basics of graph theory
   04/08/24: Part 2: Node representations: Deepwalk and node2vec
   04/15/24: Part 3: Basics of GNN - node classification
   04/22/24: Part 4: Introduction to Graph Convolutions
   04/29/24: Part 5: Introduction to Graph Attention Network

Note:  GitHub repositories and registration links will be available closer to the start date.