With an understanding of GNN architectures, we now turn to the practical mechanics of training these models. This chapter details the complete workflow for taking a defined GNN architecture and teaching it to perform a specific task on graph data.

You will learn how to configure a model for an application like node classification, which involves passing the final node embeddings through a classification head. We will then examine how to select an appropriate objective function, such as cross-entropy loss, to measure model error. The core of the chapter is dedicated to constructing a standard training loop: performing a forward pass, calculating the loss, and updating the model's weights using backpropagation.

We will also address procedures unique to graph-based learning. This includes the distinction between transductive and inductive settings for splitting data and its implications for model evaluation. You will learn to use standard metrics like accuracy to assess performance. Finally, we will discuss methods such as dropout for regularization to improve model generalization. The chapter concludes with a practical exercise where you will apply these steps to train the GCN model developed earlier.