Case Study: Learning on Heterogeneous Graphs with PyG
Ramona Bendias, Matthias Fey
Learn how to build and analyze heterogeneous graphs using PyG, a machine graph learning library in Python. This workshop will provide a hands-on introduction to the concept of heterogeneous graphs and their applications, including their ability to capture the complexity and diversity of real-world systems. Participants will gain experience in creating a heterogeneous graph from multiple data tables, preparing a dataset, and implementing and training a model using PyG.
I have a Master's degree in science and am currently working as a Applied Machine Learning Engineer at Kumo,ai, where I use my skills in machine learning and data analysis to solve challenging problems. In addition to my work at Kumo, I am also a contributor to PyG, a machine graph learning library in Python.