Course DescriptionThis course focuses on techniques for machine learning and data mining on graph data. The course starts quickly refreshing the basics of graph theory and then explores more advanced techniques of graph theory – centrality metrics (like betweenness centrality and page-rank) and community detection. The major part of the course will focus on machine learning techniques for graph and node classification. Lastly, the course will touch on the interpretability of graph machine learning models. In the practical part, we will get our hands on the selected techniques and try them with publicly available data.
- Python (PyTorch, pytorch_geometric, dgl, pandas, numpy libraries)
- Google Collab
- Machine Learning Concepts (loss, gradient descent optimization, back-propagation algorithm),
- Graph Theory Basics,
- Python (PyTorch)