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

nach der Bestellung gedruckt Neuware - Printed after ordering - Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF Elektronisches BuchKey Features:Implement state-of-the-art graph neural network architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook Description:Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.What You Will Learn:Understand the fundamental concepts of graph neural networksImplement graph neural networks using Python and PyTorch GeometricClassify nodes, graphs, and edges using millions of samplesPredict and generate realistic graph topologiesCombine heterogeneous sources to improve performanceForecast future events using topological informationApply graph neural networks to solve real-world problemsWho this book is for:This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you're new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.

Seller Inventory # 9781804617526

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

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.