Graph & Geometric Machine Learning
This workshop provides graduate students with the necessary skills for understanding and applying graph machine learning techniques. Among the covered topics, you will find the fundamentals of graph theory, practical applications of graph neural networks, and advanced methods for graph-based data analysis.
- Repo: https://github.com/ua-datalab/GraphML
- YouTube Playlist
- Mondays at 2PM: Weaver Science and Engineering Library Rm 212.
- Zoom: https://arizona.zoom.us/j/86423223879
- Qualtrics Registration: Link
| Date | Topics Covered | Instructor | Helpers | Code / Notebook |
|---|---|---|---|---|
| 04/01/24 | Graph ML Part-1 Why Graph ML and basics of graph theory | Shashank | Carlos | Colab Notebook YouTube Recording |
| 04/08/24 | Graph ML Part-2 Node representations: Deepwalk and node2vec | Shashank | Carlos | Colab Notebook YouTube Recording |
| 04/15/24 | Graph ML Part-3 Basics of GNN - Node classification | Shashank | Carlos | Colab Notebook YouTube Recording |
| 04/22/24 | Graph ML Part-4 Introduction to Graph Convolutions | Shashank | Carlos | Colab Notebook YouTube Recording |
| 04/29/24 | Graph ML Part-5 Introduction to Graph Attention | Shashank | carlos | Colab Notebook [YouTube Recording] |