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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

Part 3: Advanced Techniques

In this third part of the book, we will delve into the more advanced and specialized GNN architectures that have been developed to solve a variety of graph-related problems. We will cover state-of-the-art GNN models designed for specific tasks and domains, which can address challenges and requirements more effectively. In addition, we will provide an overview of several new graph-based tasks that can be tackled using GNNs, such as link prediction and graph classification, and demonstrate their applications through practical code examples and implementations.

By the end of this part, you will be able to understand and implement advanced GNN architectures and apply them to solve your own graph-based problems. You will have a comprehensive understanding of specialized GNNs and their respective strengths, as well as hands-on experience with code examples. This knowledge will equip you with the skills to apply GNNs to real-world use cases and potentially contribute...

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