Graph machine learning

Tamara Drucks & Franka Bause

Graphs are everywhere and form the basis for many machine learning tasks such as content moderation or drug discovery. Unlike structured data such as images or sequences, graphs often have irregular structure and variable sizes, rendering traditional machine learning models ineffective. This workshop introduces graph machine learning and Graph Neural Networks (GNNs) from both a theoretical and practical perspective. Participants will learn how to model relational data as graphs and apply modern deep learning techniques using PyTorch Geometric. The course combines short lectures on key concepts with guided hands-on coding sessions. No prior experience with graph learning is required, but basic Python knowledge is expected.