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

Classifying nodes on PubMed

In this section, we will implement a GraphSAGE architecture to perform node classification on the PubMed dataset (available under the MIT license from https://github.com/kimiyoung/planetoid) [4].

Previously, we saw two other citation network datasets from the same Planetoid family – Cora and CiteSeer. The PubMed dataset displays a similar but larger graph, with 19,717 nodes and 88,648 edges. Figure 8.3 shows a visualization of this dataset as created by Gephi (https://gephi.org/).

Figure 8.4 – A visualization of the PubMed dataset

Node features are TF-IDF-weighted word vectors with 500 dimensions. The goal is to correctly classify nodes into three categories – diabetes mellitus experimental, diabetes mellitus type 1, and diabetes mellitus type 2. Let’s implement it step by step using PyG:

We load the PubMed dataset from the Planetoid class and print some information about the graph:
```
from...
```

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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

Classifying nodes on PubMed

Authors (1)

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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

Classifying nodes on PubMed

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