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Graph Neural Networks in Action with Python: A Complete Practitioner's Guide to Building, Scaling, and Deploying GNN Applications
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- FormatePub
- ISBN8231519231
- EAN9798231519231
- Date de parution27/05/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurWalzone Press
Résumé
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 …
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. 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.
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. 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.