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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

activation function 107

Adamic-Adar index 165

adjacency list 22

adjacency matrix 20-22

adjacent 18

aggregate function 148

aggregation 129

aggregator 129

LSTM aggregator 129

mean aggregator 129

pooling aggregator 129

anomaly detection 275

A* search 25

attention scores 106

Attention Temporal Graph Convolutional Network (A3T-GCN)

implementing 267-273

autoregressive models 190, 191

average precision (AP) 169

averaging 108

batch gradient descent 126

Bayesian Personalized Ranking (BPR) 309

BERT 105

betweenness centrality 19, 20

bidirectional BFS 24

binary bag of words 68

BookCrossing community

URL 296

Book-Crossing dataset 296-302

preprocessing 302-305

breadth-first search (BFS) 11, 23, 51

implementing...

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Table of Contents (25) Chapters

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Table of Contents (25) Chapters

Index

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