Description

This course provides a technical introduction to Graph Neural Networks (GNNs), a class of models designed to perform inference on data structured as graphs. You will learn the principles behind how GNNs operate, from message passing mechanisms to specific architectures like Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs). The material covers the full process of building a GNN model: representing graph data, defining the network architecture, training the model, and evaluating its performance on common graph-related tasks. Practical sections focus on implementation using the PyTorch Geometric library.