February 2025
Intermediate to advanced
392 pages
12h 9m
English
Overview
A hands-on guide to powerful graph-based deep learning models.
Graph Neural Networks in Action teaches you to build cutting-edge graph neural networks for recommendation engines, molecular modeling, and more. This comprehensive guide contains coverage of the essential GNN libraries, including PyTorch Geometric, DeepGraph Library, and Alibaba’s GraphScope for training at scale.
In Graph Neural Networks in Action, you will learn how to:
In Graph Neural Networks in Action 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.
About the Technology
Graphs are a natural way to model the relationships and hierarchies of real-world data. Graph neural networks (GNNs) optimize deep learning for highly-connected data such as in recommendation engines and social networks, along with specialized applications like molecular modeling for drug discovery.
About the Book
Graph Neural Networks in Action teaches you how to analyze and make predictions on data structured as graphs. You’ll work with graph convolutional networks, attention networks, and auto-encoders to take on tasks like node classification, link prediction, working with temporal data, and object classification. Along the way, you’ll learn the best methods for training and deploying GNNs at scale—all clearly illustrated with well-annotated Python code!
What's Inside
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 seasoned machine learning engineer. Namid Stillman, PhD is a research scientist and machine learning engineer with more than 20 peer-reviewed publications.
Quotes
Despite their giant success in research, real-world GNN adoption remains limited. This book empowers practitioners to overcome that gap.
- Matthias Fey, Creator of PyTorch Geometric and Kumo.AI
Your roadmap to cutting-edge graph-based learning.
- Maxime Dehaut, Luxembourg Stock Exchange
A hands-on guide that bridges academic concepts and real-world applications. I recommend it.
- Victor Dibia, Microsoft Research
Graph Neural Networks in Action teaches you to build cutting-edge graph neural networks for recommendation engines, molecular modeling, and more. This comprehensive guide contains coverage of the essential GNN libraries, including PyTorch Geometric, DeepGraph Library, and Alibaba’s GraphScope for training at scale.
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
In Graph Neural Networks in Action 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.
About the Technology
Graphs are a natural way to model the relationships and hierarchies of real-world data. Graph neural networks (GNNs) optimize deep learning for highly-connected data such as in recommendation engines and social networks, along with specialized applications like molecular modeling for drug discovery.
About the Book
Graph Neural Networks in Action teaches you how to analyze and make predictions on data structured as graphs. You’ll work with graph convolutional networks, attention networks, and auto-encoders to take on tasks like node classification, link prediction, working with temporal data, and object classification. Along the way, you’ll learn the best methods for training and deploying GNNs at scale—all clearly illustrated with well-annotated Python code!
What's Inside
- Train and deploy a graph neural network
- Generate node embeddings
- Use GNNs for very large datasets
- Build a graph data pipeline
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 seasoned machine learning engineer. Namid Stillman, PhD is a research scientist and machine learning engineer with more than 20 peer-reviewed publications.
Quotes
Despite their giant success in research, real-world GNN adoption remains limited. This book empowers practitioners to overcome that gap.
- Matthias Fey, Creator of PyTorch Geometric and Kumo.AI
Your roadmap to cutting-edge graph-based learning.
- Maxime Dehaut, Luxembourg Stock Exchange
A hands-on guide that bridges academic concepts and real-world applications. I recommend it.
- Victor Dibia, Microsoft Research