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Hands-On Graph Neural Networks Using Python

You're reading from  Hands-On Graph Neural Networks Using Python

Product type Book
Published in Apr 2023
Publisher Packt
ISBN-13 9781804617526
Pages 354 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Maxime Labonne Maxime Labonne
Profile icon Maxime Labonne
LinkedIn
Maxime Labonne is currently a senior applied researcher at Airbus. He received a M.Sc. degree in computer science from INSA CVL, and a Ph.D. in machine learning and cyber security from the Polytechnic Institute of Paris. During his career, he worked on computer networks and the problem of representation learning, which led him to explore graph neural networks. He applied this knowledge to various industrial projects, including intrusion detection, satellite communications, quantum networks, and AI-powered aircrafts. He is now an active graph neural network evangelist through Twitter and his personal blog.
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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 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 24. Other Books You May Enjoy

Further reading

  • [1] M. Ring, S. Wunderlich, D. Grüdl, D. Landes, and A. Hotho, Flow-based benchmark data sets for intrusion detection, in Proceedings of the 16th European Conference on Cyber Warfare and Security (ECCWS), ACPI, 2017, pp. 361–369.
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Authors (1)

Profile icon Maxime Labonne
LinkedIn
Maxime Labonne is currently a senior applied researcher at Airbus. He received a M.Sc. degree in computer science from INSA CVL, and a Ph.D. in machine learning and cyber security from the Polytechnic Institute of Paris. During his career, he worked on computer networks and the problem of representation learning, which led him to explore graph neural networks. He applied this knowledge to various industrial projects, including intrusion detection, satellite communications, quantum networks, and AI-powered aircrafts. He is now an active graph neural network evangelist through Twitter and his personal blog.
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See other products by Maxime Labonne

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