A hands-on guide to powerful graph-based deep learning models! Learn how to build cutting-edge graph neural networks for recommendation engines, molecular modeling, and more.

In Graph Neural Networks in Action, you will learn how to:

• Train and deploy a graph neural network
• Generate node embeddings
• Use GNNs at scale for very large datasets
• Build a graph data pipeline
• Create a graph data schema
• Understand the taxonomy of GNNs
• Manipulate graph data with NetworkX

Graph Neural Networks in Action teaches you to create powerful deep learning models for working with graph data. You’ll learn how to both design and train your models, and how to develop them into practical applications you can deploy to production. Go hands-on and explore relevant real-world projects as you dive into graph neural networks perfect for node prediction, link prediction, and graph classification. Inside this practical guide, you’ll explore common graph neural network architectures and cutting-edge libraries, all clearly illustrated with well-annotated Python code.