The previous chapters established the operational principles of GNNs, including implementing a message passing layer from scratch. While instructive, this manual approach is not practical for building and testing different architectures. This chapter transitions to a higher level of abstraction using PyTorch Geometric (PyG), a dedicated library that simplifies the development of graph-based models. PyG provides optimized implementations of common GNN layers and data handling utilities, allowing you to focus on model design rather than low-level implementation details.

Throughout this chapter, you will learn to use the main components of PyTorch Geometric. We will start with the Data object, which is how PyG represents an entire graph. You will then see how to load standard graph datasets and construct a GNN model using PyG's pre-built layers, such as GCNConv and GATConv. We will also cover how the library handles the batching of graphs for efficient training.

By the end of this chapter, you will be able to write a complete training and evaluation script for a node classification task. The final hands-on section will apply these skills to build a GNN on the Cora citation network, a standard benchmark dataset.