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Hands-On Graph Neural Networks Using Python

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

Further reading

[1] H. Tong, C. Faloutsos and J. -y. Pan. “Fast Random Walk with Restart and Its Applications” in Sixth International Conference on Data Mining (ICDM’06), 2006, pp. 613-622, doi: 10.1109/ICDM.2006.70.
[2] Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (August 2009), 30–37. https://doi.org/10.1109/MC.2009.263.
[3] J. Qiu, Y. Dong, H. Ma, J. Li, K. Wang, and J. Tang. Network Embedding as Matrix Factorization. Feb. 2018. doi: 10.1145/3159652.3159706.
[4] D. P. Kingma and M. Welling. Auto-Encoding Variational Bayes. arXiv, 2013. doi: 10.48550/ARXIV.1312.6114.
[5] T. N. Kipf and M. Welling. Variational Graph Auto-Encoders. arXiv, 2016. doi: 10.48550/ARXIV.1611.07308.
[6] M. Zhang and Y. Chen. Link Prediction Based on Graph Neural Networks. arXiv, 2018. doi: 10.48550/ARXIV.1802.09691.
[7] Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin...

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Authors (1)

Maxime Labonne

Maxime Labonne

Maxime Labonne is a Senior Staff Machine Learning Scientist at Liquid AI, serving as the head of post-training. He holds a Ph.D. in Machine Learning from the Polytechnic Institute of Paris and is recognized as a Google Developer Expert in AI/ML. An active blogger, he has made significant contributions to the open-source community, including the LLM Course on GitHub, tools such as LLM AutoEval, and several state-of-the-art models like NeuralBeagle and Phixtral. He is the author of the best-selling book “Hands-On Graph Neural Networks Using Python,” published by Packt.

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