While understanding GNN architectures is fundamental, training them effectively, especially at scale, presents distinct challenges. Issues like oversmoothing, where node representations converge undesirably, and oversquashing, which limits information propagation across the graph, can significantly degrade performance. Training GNNs on very large graphs also introduces substantial computational and memory burdens.

This chapter focuses on these practical training complexities. We will examine the theoretical underpinnings of problems like oversmoothing and oversquashing and discuss methods to counteract them, such as architectural modifications and specific training techniques. You will also learn strategies for scaling GNN training to massive datasets, including neighborhood sampling (e.g., GraphSAGE), graph sampling (e.g., GraphSAINT), and graph clustering approaches (e.g., Cluster-GCN). Finally, we'll touch upon relevant optimization considerations for these models.