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Hands-On Graph Neural Networks Using Python

You're reading from   Hands-On Graph Neural Networks Using Python Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch

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Product type Paperback
Published in Apr 2023
Publisher Packt
ISBN-13 9781804617526
Length 354 pages
Edition 1st Edition
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Author (1):
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Maxime Labonne Maxime Labonne
Author Profile Icon Maxime Labonne
Maxime Labonne
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Table of Contents (25) Chapters Close

Preface 1. Part 1: Introduction to Graph Learning
2. Chapter 1: Getting Started with Graph Learning FREE CHAPTER 3. Chapter 2: Graph Theory for Graph Neural Networks 4. Chapter 3: Creating Node Representations with DeepWalk 5. Part 2: Fundamentals
6. Chapter 4: Improving Embeddings with Biased Random Walks in Node2Vec 7. Chapter 5: Including Node Features with Vanilla Neural Networks 8. Chapter 6: Introducing Graph Convolutional Networks 9. Chapter 7: Graph Attention Networks 10. Part 3: Advanced Techniques
11. Chapter 8: Scaling Up Graph Neural Networks with GraphSAGE 12. Chapter 9: Defining Expressiveness for Graph Classification 13. Chapter 10: Predicting Links with Graph Neural Networks 14. Chapter 11: Generating Graphs Using Graph Neural Networks 15. Chapter 12: Learning from Heterogeneous Graphs 16. Chapter 13: Temporal Graph Neural Networks 17. Chapter 14: Explaining Graph Neural Networks 18. Part 4: Applications
19. Chapter 15: Forecasting Traffic Using A3T-GCN 20. Chapter 16: Detecting Anomalies Using Heterogeneous GNNs 21. Chapter 17: Building a Recommender System Using LightGCN 22. Chapter 18: Unlocking the Potential of Graph Neural Networks for Real-World Applications
23. Index 24. Other Books You May Enjoy

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