Graph Neural Networks in Action

A hands-on guide to powerful graph-based deep learning models! Graph Neural Networks in Action is a great guide about how to build cutting-edge graph neural networks and powerful deep learning models for recommendation engines, molecular modeling, and more. You will learn how to both design and train your models, and how to develop them into practical applications you can deploy to production. Ideal for Python programmers, you will also explore common graph neural network architectures and cutting-edge libraries, all clearly illustrated with well-annotated Python code. The main features include: * 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 Go hands-on and explore relevant real-world projects as you dive into graph neural networks perfect for node prediction, link prediction, and graph