This book shows how to to build graph neural networks for recommendation engines and molecular modeling.  TKeita Broadwater and Namid Stillman show how to both design and train models, and how to develop them into practical applications, with graph neural networks for node prediction, link prediction, and graph classification. The book includes coverage of the essential GNN libraries, including PyTorch Geometric, DeepGraph Library, and Alibaba’s GraphScope for training at scale.

Author: Keita Broadwater and Namid Stillman 
Publisher: Manning
Date: April 2025
Pages: 392
ISBN: 978-1617299056
Print: 1617299057
Kindle: B0DY967PSN
Audience: Developers interested in graph neural networks
Level: Intermediate
Category: Artificial Intelligence

Topics 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

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