Hands On Graph Neural Networks Using Python Github

Bridging Theory and Practice: ML Solutions for Today’s Challenges 3 days, 20+ experts, and 25+ tech sessions and talks covering critical aspects of: The future of AI is unfolding. Don’t fall behind. Stay ahead with DataPro, the free weekly newsletter for data scientists, AI/ML researchers, and data engineers. From trending tools like PyTorch, scikit-learn, XGBoost, and BentoML to hands-on insights on database optimization and real-world ML workflows, you’ll get what matters, fast.

Stay sharp with DataPro. Join 115K+ data professionals who never miss a beat. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python This document provides an introduction to the "Hands-On Graph Neural Networks Using Python" repository, which contains implementation code and examples for practical Graph Neural Network (GNN) techniques using PyTorch and PyTorch Geometric.

The repository serves as a companion to the book published by Packt and covers a complete learning path from fundamental graph concepts to advanced GNN applications. For detailed instructions on setting up the required environment, see Environment Setup. The repository focuses on practical implementation of graph neural networks for analyzing data that can be represented as graphs, such as social networks, chemical compounds, and transportation networks. It covers: The material follows a progressive learning approach, starting with basic graph theory concepts and advancing to state-of-the-art GNN architectures and techniques. Welcome to this hands-on workshop, where we’ll delve into the exciting world of Graph Neural Networks (GNNs)!

GNNs have revolutionized Machine Learning by enabling effective analysis of complex graph-structured data. To help you get started, I’ve curated a concise guide (study sheet) that will introduce you to the fundamentals of GNNs and bring you up-to-speed with the latest advancements. Gentle Introduction to GNNs (by Distill) Understanding Convolutions on Graphs (by Distill) Math Behind Graph Neural Networks (by Rishabh Anand) Hands-on graph neural networks using Python : practical techniques and architectures for building powerful graph and deep learning apps with Pytorch Labonne, Maxime, 2023.

PDF Hands-On Graph Neural Networks Using Python is a comprehensive guide for machine learning practitioners, data scientists, and students interested in learning about graph neural networks and their applications. The book begins with the fundamentals of graph theory and shows how to create graph datasets from tabular data. It then explores major GNN architectures and essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. The book concludes with applications to solve real-life problems, enabling readers to build a professional portfolio. By the end of this book, readers will have learned to create graph datasets, implement GNNs using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training GNN...

The entire code with examples and use cases is freely available on GitHub. Additionally, we recommend Maxime's blog posts on Graph Neural Networks: 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. 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. Get instant access to a library of pre-built books—free trial, no credit card required. Start training your team in minutes! Transforming learning experiences with AI-powered intelligence for modern organizations. Deliver personalized training, identify skill gaps, and accelerate professional development. Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and apps Purchase of the print or Kindle book includes a free PDF eBook

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. 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. You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

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