Graph Neural Networks with PyTorch For Beginners: A Practical Guide to Graph Learning, GNN Architectures, Training, and Real-World Applications (PyTorch Programming Series)

Graph Neural Networks with PyTorch For Beginners introduces the concepts and practical techniques needed to understand and build Graph Neural Networks using PyTorch-based tools and workflows.
Traditional neural networks work naturally with images, sequences, and other structured data, but many real-world problems involve relationships between entities. Social networks, recommendation systems, knowledge graphs, molecular structures, fraud networks, and transportation systems can all be represented as graphs. Graph Neural Networks provide a powerful approach for learning from both the features of individual entities and the relationships connecting them.
This book introduces graph concepts before progressively exploring graph representations, message passing, graph convolution, node classification, link prediction, graph classification, graph attention, graph datasets, training workflows, and practical GNN applications.
What You Will Learn

• Understand graphs, nodes, edges, features, and graph representations
• Learn the foundations of Graph Neural Networks
• Understand message-passing neural networks
• Build GNN models with PyTorch-based tooling
• Work with graph datasets and graph structures
• Perform node classification and link prediction
• Build graph-level classification models
• Explore graph convolution and attention mechanisms
• Train, evaluate, and improve GNN models
• Handle common challenges in graph machine learning
• Apply GNNs to practical machine learning problems
• Develop a foundation for more advanced graph learning and GraphRAG systems

Designed for readers who already have some familiarity with Python and basic machine learning concepts, this book provides an accessible path into graph-based deep learning while maintaining a strong emphasis on practical implementation.
By the end of the book, readers will understand how Graph Neural Networks work, how to implement them, and how to approach real-world problems where relationships and connectivity are as important as the data attached to individual entities.