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

Product type Paperback

Published in Apr 2023

Publisher Packt

ISBN-13 9781804617526

Length 354 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Deep Learning

Author (1):

Maxime Labonne

View More author details

Table of Contents (25) Chapters

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

Why subscribe?

24. Other Books You May Enjoy

Improving Embeddings with Biased Random Walks in Node2Vec

Node2Vec is an architecture largely based on DeepWalk. In the previous chapter, we saw the two main components of this architecture: random walks and Word2Vec. How can we improve the quality of our embeddings? Interestingly enough, not with more machine learning. Instead, Node2Vec brings critical modifications to the way random walks themselves are generated.

In this chapter, we will talk about these modifications and how to find the best parameters for a given graph. We will implement the Node2Vec architecture and compare it to using DeepWalk on Zachary’s Karate Club. This will give you a good understanding of the differences between the two architectures. Finally, we will use this technology to build a real application: a movie recommender system (RecSys) powered by Node2Vec.

By the end of this chapter, you will know how to implement Node2Vec on any graph dataset and how to select good parameters. You will understand...

The rest of the chapter is locked

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

Table of Contents (25) Chapters

Improving Embeddings with Biased Random Walks in Node2Vec

Authors (1)

Personalised recommendations for you

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

Table of Contents (25) Chapters

Improving Embeddings with Biased Random Walks in Node2Vec

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you