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.
about the technology
Graph neural networks expand the capabilities of deep learning beyond traditional tabular data, text, and images. This exciting new approach brings the amazing capabilities of deep learning to graph data structures, opening up new possibilities for everything from recommendation engines to pharmaceutical research.
about the book
In Graph Neural Networks in Action you’ll create deep learning models that are perfect for working with interconnected graph data. Start with a comprehensive introduction to graph data’s unique properties. Then, dive straight into building real-world models, including GNNs that can generate node embeddings from a social network, recommend eCommerce products, and draw insights from social sites. This comprehensive guide contains coverage of the essential GNN libraries, including PyTorch Geometric, DeepGraph Library, and Alibaba’s GraphScope for training at scale.
about the reader
For Python programmers familiar with machine learning and the basics of deep learning.
about the authors
Keita Broadwater, PhD, MBA is a machine learning engineer with over ten years executing data science, analytics, and machine learning applications and projects. He is Chief of Machine Learning at candidates.ai, a firm which uses AI to enhance executive search. Dr. Broadwater has delivered DS and ML projects for all types of organizations, from small startups to Fortune 500 companies, and has developed and advised on graph-related projects in the industries of insurance, HR and recruiting, and supply chain.