Textbook in PDF format Graph Neural Networks in Action with Python: A Complete Practitioner's Guide to Building, Scaling, and Deploying GNN Applications is the definitive comprehensive resource for mastering graph neural networks from fundamentals to production deployment. This extensive guide expands far beyond traditional GNN introductions, offering 12 detailed chapters with 8 practical sections each, covering everything from basic graph theory to advanced industrial applications. Unlike existing books that focus primarily on theory, this practitioner-oriented guide emphasizes real-world implementation using the latest Python frameworks including PyTorch Geometric, DGL, and GraphScope. You'll master core architectures like Graph Convolutional Networks, GraphSAGE, and Graph Attention Networks, then advance to cutting-edge topics including heterogeneous graphs, temporal networks, and large-scale distributed training. The book provides hands-on experience with complete MLOps pipelines, covering model serving, monitoring, and production deployment strategies. Each chapter includes specific framework recommendations and detailed implementation guidance for building recommendation systems, molecular modeling applications, fraud detection systems, and social network analysis tools. Graph-structured data forms the foundation of Graph Neural Networks (GNNs), representing information as interconnected entities rather than isolated data points. Unlike traditional data formats, graphs capture relationships explicitly through their structure, making them ideal for problems where connections matter as much as individual attributes. From mapping social interactions to modeling molecular structures, graphs provide a natural representation for complex systems. As we explore the fundamentals of graph theory and various real-world applications, we’ll discover when and why graph-based approaches offer significant advantages over conventional methods. This understanding will serve as essential groundwork for implementing effective GNN solutions to complex problems across diverse domains. Graphs appear throughout our world, often in places we might not immediately recognize. At their core, graphs consist of nodes (also called vertices) and edges. Nodes represent entities or objects, while edges represent relationships or connections between these entities. This simple yet powerful abstraction allows us to model complex systems with remarkable clarity. Perfect for Python developers, data scientists, and machine learning engineers seeking to leverage graph-structured data, this book bridges the gap between academic research and practical application. With comprehensive coverage of scaling techniques, performance optimization, and real-world case studies, you'll gain the expertise needed to deploy graph neural networks in production environments successfully. Deploying Gnn Applications Preface Introduction to Graph Neural Networks Graph Data Structures and Manipulation GraphSAGE and Inductive Learning GraphSAINT and Advanced Sampling Graph Autoencoders and Generative Models Dynamic and Temporal Graph Networks Heterogeneous and Multi-Modal Graphs Large-Scale Graph Processing Production Deployment and MLOps Advanced Applications and Case Studies