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

Predicting links with SEAL

The previous section introduced node-based methods, which learn relevant node embeddings to compute link likelihoods. Another approach consists of looking at the local neighborhood around the target nodes. These techniques are called subgraph-based algorithms and were popularized by SEAL (which could be said to stand for Subgraphs, Embeddings, and Attributes for Link prediction – though not always!). In this section, we will describe the SEAL framework and implement it using PyTorch Geometric.

Introducing the SEAL framework

Introduced in 2018 by Zhang and Chen [6], SEAL is a framework that learns graph structure features for link prediction. It defines the subgraph formed by the target nodes and their -hop neighbors as the enclosing subgraph. Each enclosing subgraph is used as input (instead of the entire graph) to predict a link likelihood. Another way to look at it is that SEAL automatically learns a local heuristic for link prediction.

...