Part 3 Advanced topics

The evolution of graph neural networks (GNNs) has unlocked a wealth of new possibilities, and this part of the book delves into some of the most exciting and complex frontiers. We begin by examining spatiotemporal GNNs, which model dynamic graphs that evolve over time, along with applications such as pose estimation in motion analysis. Next, we tackle the challenge of scaling GNNs to massive datasets, exploring strategies to efficiently process industrial-scale graphs while maintaining high performance. Finally, we focus on the practical considerations for building and deploying GNN projects, including how to create graph data models from nongraph data, perform ETL (extract, transform, load) and preprocessing from raw data sources, and construct datasets and data loaders with PyTorch Geometric (PyG). Each chapter in this part provides actionable insights and tools to master these advanced topics, empowering you to unlock the full potential of GNNs in your work.