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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

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Frequently bought together

Causal Inference and Discovery in Python

€40.99

Interpretable Machine Learning with Python

€37.99

Hands-On Graph Neural Networks Using Python

€37.99

Total € 116.97

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Amazon verified reviews

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.

Amazon Verified review

Madhav Jariwala Jun 07, 2023

I began my journey with Graph Neural Networks approximately 1.5 years ago, and during that period, there were only a handful of resources accessible for acquiring knowledge about GNN. At that time, comprehending the structure of GNN, which significantly differs from conventional neural networks, proved to be quite challenging. In hindsight, I fervently wish that a book of this caliber had been available to me back then.This remarkable book thoroughly covers all the fundamental concepts necessary to grasp GNN effectively. The presentation of the code is methodical, making it easier for readers to follow along. Moreover, the inclusion of Jupyter Notebook files on GitHub allows for seamless testing of the code on Google Colab, thereby enhancing the practical learning experience.A particular highlight of this book is its precise and easily comprehensible derivation of equations. The authors have taken great care to ensure that readers can grasp the mathematical foundations of GNN without unnecessary confusion. This attention to detail greatly aids in understanding and applying GNN in real-world scenarios.In summary, without a shadow of a doubt, I assert that this book stands as the epitome of excellence for individuals seeking to gain proficiency in Graph Neural Networks. Its comprehensive coverage of essential concepts, well-structured code explanations, availability of runnable code on GitHub, clear derivations of equations, and abundance of real-world examples collectively establish it as the ultimate learning resource for aspiring GNN practitioners.

Evgenia P. Jun 17, 2023

If you're keen on exploring Graph Neural Networks, "Hands-on Graph Neural Networks Using Python" is an excellent resource for you. The book offers practical insights and step-by-step guidance for building graph and deep learning applications using PyTorch. Whether you're new to this field or an experienced practitioner, this comprehensive guide has something for everyone.The book takes a practical approach to introduce readers to GNNs, even if they lack extensive coding experience. The author explains complex concepts in a clear and concise manner, making them easy to comprehend. The Python code examples, available as Jupyter Notebooks, are beneficial for implementing and experimenting with GNNs. By following the examples, readers can develop a strong understanding of GNN implementation details. Furthermore, the book introduces readers to the Pytorch-geometric library, which is a bonus.The book is divided into four parts, providing readers with a well-structured learning experience. The first part serves as an introduction, laying the foundation for understanding GNNs. The following sections cover fundamental techniques, advanced approaches, and practical applications. Real-world examples and engaging projects are included to help readers apply their newly acquired knowledge effectively and enrich the learning process.In conclusion, "Hands-on Graph Neural Networks Using Python" is an incredibly useful guide for those interested in exploring and utilizing the potential of GNNs. Its practical approach, lucid explanations, and extensive coverage make it effortless for readers to build graphs and deep learning applications using PyTorch.

Amazon Customer Jun 19, 2023

"Hands-On Graph Neural Networks in Python" is an invaluable resource for anyone interested in understanding and implementing GNNs. The author's expertise shines as he strikes a perfect balance between theory and practical examples. From beginner-friendly explanations to advanced techniques, the book covers it all. Real-world applications, such as social network analysis and recommendation systems, provide inspiration while reinforcing concepts. The concise code snippets and downloadable files make it easy to follow along and experiment. In conclusion, this book is a must-have guide for data scientists, machine learning practitioners, and researchers, unlocking the potential of graph-based machine learning. Highly recommended.

Amazon Customer May 08, 2023

I thought this book provided a great overview of graph neural networks. For context, I have a lot of experience using neural networks, especially in computer vision, but I am new to graph neural networks.In the first chapter, it does a good job starting from the beginning and giving good use cases for each algorithm and graph learning technique. I understood why you would choose to use a graph neural network. The second chapter goes into theory, but nothing complicated. You should already know it if you come from a Computer Science background (although my degree is 20 years old at this point) but even if you don’t have a Computer Science background, it is easy to understand. Even though it is theory, they start showing you code early on. I like that and they explain the code well. Also the code is not for low-level implementations of everything. For the most part you are using high-level libraries instead of coding numpy arrays so it made the whole process much easier to understand and more like how you would actually program something. The next couple chapters go into simple graph neural networks. They are a lot easier to program then I thought. Essentially, for beginning algorithms you feed a neural network not only the input but also an adjacency matrix (a matrix of 1’s and 0’s that shows you which nodes are connected) and PyTorch Geometric does the rest for you. The next chapters get into more advanced techniques including Graph Attention Networks and GraphSAGE. I didn’t know what they were either, but the book explains it - even if it is a little harder to follow. The last part of the book goes over examples and the code you need to write to solve standard graph neural network problems such as forecasting traffic or detecting anomalies.Overall, writer is very knowledgeable about the topic, the code is clear and high-level, and the book is definitely worth the read.

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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