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.
publication date
2023-04-14
what you will learn
Understand the fundamental concepts of graph neural networks
Implement graph neural networks using Python and PyTorch Geometric
Classify nodes, graphs, and edges using millions of samples
Predict and generate realistic graph topologies
Combine heterogeneous sources to improve performance
Forecast future events using topological information
Apply graph neural networks to solve real-world problems
key features
Implement state-of-the-art graph neural network architectures in Python * Create your own graph datasets from tabular data * Build powerful traffic forecasting, recommender systems, and anomaly detection applications
approach
Through hands-on examples and code snippets, readers will learn how to build and train graph neural network models, and apply these techniques to real-world applications. The book is structured in a way that allows readers to gradually build their knowledge and skills, starting from the basics and progressing to more complex and challenging topics.
audience
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.
short description
Hands-On Graph Neural Networks Using Python is a comprehensive guide to building and training graph neural networks for a variety of real-world applications. With clear explanations and plenty of hands-on examples, this book is a valuable resource for anyone looking to learn about and apply graph neural networks to their own projects.
subtitle
Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
keywords
Data analysis; deep learning book; GNN; PyTorch book; python data analysis; PyTorch deep learning; pytorch computer vision
Product ISBN
9781804617526
