You're reading from Hands-On Graph Neural Networks Using Python

Product type Book

Published in Apr 2023

Publisher Packt

ISBN-13 9781804617526

Pages 354 pages

Edition 1st Edition

Languages

Concepts

Neural Networks

Author (1):

Maxime Labonne

Table of Contents (25) Chapters

Preface

1. Part 1: Introduction to Graph Learning

2. Chapter 1: Getting Started with Graph Learning

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

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24. Other Books You May Enjoy

DeepWalk and random walks

Proposed in 2014 by Perozzi et al., DeepWalk quickly became extremely popular among graph researchers. Inspired by recent advances in NLP, it consistently outperformed other methods on several datasets. While more performant architectures have been proposed since then, DeepWalk is a simple and reliable baseline that can be quickly implemented to solve a lot of problems.

The goal of DeepWalk is to produce high-quality feature representations of nodes in an unsupervised way. This architecture is heavily inspired by Word2Vec in NLP. However, instead of words, our dataset is composed of nodes. This is why we use random walks to generate meaningful sequences of nodes that act like sentences. The following diagram illustrates the connection between sentences and graphs: