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

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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

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24. Other Books You May Enjoy

Predicting cases of COVID-19

This section will focus on a new application with epidemic forecasting. We will use the England Covid dataset, a dynamic graph with temporal information introduced by Panagopoulos et al. in 2021 [3]. While nodes are static, connections between and edge weights vary over time. This dataset represents the number of reported cases of COVID-19 in 129 England NUTS 3 regions between March 3 and May 12, 2020. Data was collected from mobile phones that installed the Facebook application and shared their location history. Our goal is to predict the number of cases in each node (region) in 1 day.

Figure 13.8 – NUTS 3 areas in England are colored in red

This dataset represents England as a graph . Due to the temporal nature of this dataset, it is composed of multiple graphs corresponding to each day of the studied period . In these graphs, node features correspond to the number of cases in each of the past days in this region...

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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

Predicting cases of COVID-19

Authors (1)

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Table of Contents (25) Chapters

Predicting cases of COVID-19

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Authors (1)

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