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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 Part 1: Introduction to Graph Learning
Chapter 1: Getting Started with Graph Learning Chapter 2: Graph Theory for Graph Neural Networks Chapter 3: Creating Node Representations with DeepWalk Part 2: Fundamentals
Chapter 4: Improving Embeddings with Biased Random Walks in Node2Vec Chapter 5: Including Node Features with Vanilla Neural Networks Chapter 6: Introducing Graph Convolutional Networks Chapter 7: Graph Attention Networks Part 3: Advanced Techniques
Chapter 8: Scaling Up Graph Neural Networks with GraphSAGE Chapter 9: Defining Expressiveness for Graph Classification Chapter 10: Predicting Links with Graph Neural Networks Chapter 11: Generating Graphs Using Graph Neural Networks Chapter 12: Learning from Heterogeneous Graphs Chapter 13: Temporal Graph Neural Networks Chapter 14: Explaining Graph Neural Networks Part 4: Applications
Chapter 15: Forecasting Traffic Using A3T-GCN Chapter 16: Detecting Anomalies Using Heterogeneous GNNs Chapter 17: Building a Recommender System Using LightGCN Chapter 18: Unlocking the Potential of Graph Neural Networks for Real-World Applications
Index Other Books You May Enjoy

Technical requirements

All the code examples from this chapter can be found on GitHub at https://github.com/PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python/tree/main/Chapter16.

The installation steps required to run the code on your local machine can be found in the Preface of this book. This chapter requires a large amount of GPU. You can lower it by decreasing the size of the training set in the code.

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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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