Hands-On Graph Neural Networks Using Python | Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch

Maxime Labonne

ISBN 10: 1804617520 / ISBN 13: 9781804617526

Published by Packt Publishing, 2023

About this Item

Hands-On Graph Neural Networks Using Python | Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch | Maxime Labonne | Taschenbuch | Englisch | 2023 | Packt Publishing | EAN 9781804617526 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 126804793

Bibliographic Details

• Title: Hands-On Graph Neural Networks Using Python | Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
• Publisher: Packt Publishing
• Publication Date: 2023
• Language: English
• ISBN 10: 1804617520
• ISBN 13: 9781804617526
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 20D millimeters 235 millimeters width by 191 millimeters height by 20 millimeters depth
• Weight: 661 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Build previously impossible applications using graph data and become an expert in graph neural networks

Key Features

• Learn the fundamentals of graph theory for data science and machine learning
• Implement state-of-the-art graph neural network architectures
• Build creative and powerful applications in various fields

Book Description

Only ten years after their creation, graph neural networks have become one of the most interesting architectures in deep learning. They have revolutionized multi-billion-dollar industries like drug discovery, where they predicted a brand-new antibiotic named Halicin. Tech companies are now trying to apply them everywhere: recommender systems for food, videos, and romantic partners; fake news detection, chip design, and 3D reconstruction.

In Graph Neural Networks, we will explore the fundamentals of graph theory and create our own datasets from raw or tabular data. We will introduce major graph neural network architectures to understand crucial concepts like graph convolution and self-attention. This knowledge will then be applied to understand and implement more specialized models, designed for various tasks (including link prediction and graph classification) or contexts (spatio-temporal data, heterogeneous graphs, and so on). Finally, we will solve real-life problems using this technology and start building a professional portfolio.

By the end of this book, you will become a Graph Neural Network expert. You will be able to reframe your problems to leverage the unreasonable effectiveness of this architecture. With these skills, you will create unique solutions using novel, state-of-the-art approaches.

What you will learn

• Create your own graph datasets from tabular or raw data
• Transform nodes and edges into high-quality embeddings
• Implement graph neural networks using PyTorch Geometric
• Select the best graph neural network model according to your problem
• Perform tasks like node classification, graph generation, link prediction
• Apply this knowledge to real use cases with raw data

Who This Book Is For

If you’re interested in machine learning, Graph Neural Networks will unlock a whole range of applications that were previously impossible. Students, data scientists, and machine learning and deep learning experts will find clear and illustrated explanations with code and notebooks to get a head start. With minimal knowledge of Python and linear algebra, you will acquire highly valuable expertise in one of the most popular architectures in AI.

Table of Contents

1. Getting Started with Graph Learning
2. Graph Theory for Graph Neural Networks
3. Creating Node Representations with DeepWalk
4. Node2Vec
5. Vanilla Neural Network
6. Normalizing Features with Graph Convolutional Networks
7. Graph Attention Network
8. Scaling Graph Neural Networks
9. Graph Classification
10. Link Prediction
11. Graph Generation
12. Learning from Heterogeneous Graphs
13. Temporal Graph Neural Networks
14. Explainability
15. Traffic Forecasting
16. Anomaly Detection
17. Recommender Systems

About the Author

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.