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Key benefits

Implement -of-the-art graph neural architectures in Python

Create your own graph datasets from tabular data

Build powerful traffic forecasting, recommender systems, and anomaly detection applications

Description

Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as 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.

Who is this book 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.

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

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

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Frequently bought together

Hands-On Graph Neural Networks Using Python

zł201.99

Interpretable Machine Learning with Python

zł201.99

Causal Inference and Discovery in Python

zł161.99

Total zł 565.97

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Amazon verified reviews

SEAN W. GRANT Jun 19, 2023

Trying to understand what graph neural networks (GNN's) are, let alone how to use them can be difficult and intimidating. In recent times, GNN's have become a hot topic and this book does a great job of introducing them and showing use cases of how they can be helpful to provide deeper insights. It is geared towards folks with a technical background and are comfortable with python (hence the "Hands On" part). I found the illustrations to be very helpful in explaining the technical concepts more and the links to further reading were good to reference if I wanted to go deeper on some topics. One part I really enjoyed was the spatio-temporal GNN on dynamic graphs. This has a lot of real-world applications, and the author did a fantastic job of explaining the problem, describing the GNN and its limitations then walking through the code to get a prediction.Overall this is a must have book to have on the shelf if you want to start to dive into the world of deep learning on graphs!

Amazon Verified review

H2N Oct 05, 2023

The book stands as a guiding light for those exploring the realm of GNN. Effortlessly merging basic graph theory with advanced methods, it provides a hands-on exploration of its applications, enriched by real-world instances. The inclusion of interactive Jupyter Notebooks on GitHub enhances the reader's journey, facilitating hands-on experiments on platforms such as Google Colab. Harmonizing theoretical insights with practical demonstrations, it's a must-have for anyone, whether new or experienced in GNN. A top recommendation!

Steven Fernandes Jul 04, 2023

This book comprehensively introduces graph neural networks, effectively demystifying complex concepts. Its practical approach to implementation using Python and PyTorch Geometric is commendable. Readers will master classifying nodes, graphs, and edges with millions of samples, and predicting realistic graph topologies. Particularly noteworthy is the book's focus on performance improvement via heterogeneous sources and applying topological information for future event forecasting. The real-world problem-solving focus of the book adds a pragmatic edge, making it an invaluable resource for both novice and experienced practitioners in the field. It's a must-read for anyone interested in graph neural networks.

Manu Jun 16, 2023

This is one of the best books to pick up Graph Neural Networks today. The book takes the reader from basics of Graphs to modern techniques like Graph Attention Networks. the the special focus on applications shows the reader how GNNs can be used in a wide variety of highly relevant use cases like forecasting, recommendation systems, etc.The book is well-written and is a pleasure to read. Would definitely recommend this book to others.

Amazon Customer Aug 15, 2023

All and all a very nice book about GNNs. This is a practice-oriented book with well-chosen examples. It's an ideal complement to practice and get the code right. There are a few typos here and there, but they're minor and the code works!

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

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