Graph Machine Learning Mastery
A Complete Guide to Graph Neural Networks, Graph Transformers, Temporal GNNs, and LLM-Powered Graph AI with PyTorch Geometric & DGLGraph-structured data powers today's most advanced AI systems-from recommendation engines and fraud detection to drug discovery, cybersecurity, and large-scale knowledge graphs. Graph Machine Learning Mastery is the definitive, end-to-end guide for engineers, researchers, and data scientists who want to design, train, scale, and deploy production-ready graph AI systems using state-of-the-art techniques.
This book goes far beyond theory. You'll master Graph Neural Networks (GNNs), Graph Transformers, Temporal & Dynamic Graph Models, and LLM-augmented Graph AI, all with hands-on implementations using industry-standard frameworks like and .
What You'll Learn- Build powerful GNN architectures: GCN, GAT, GraphSAGE, GIN, heterogeneous and large-scale GNNs
- Transition from GNNs to Graph Transformers with positional encodings and attention mechanisms
- Model temporal and dynamic graphs using TGN, TGAT, DySAT, and continuous-time message passing
- Design LLM + GNN hybrid systems for reasoning, knowledge graphs, and GraphRAG pipelines
- Apply graph ML to real-world domains: fraud detection, recommender systems, molecular graphs, finance, telecom, and cybersecurity
- Train, optimize, monitor, and deploy graph models in production environments
- Integrate GNNs with graph databases, MLOps pipelines, and scalable inference system.
Hands-On, End-to-End Projects You'll implement complete production-grade projects including:
- Node classification, graph classification, and link prediction
- Temporal graph forecasting
- Molecular property prediction with OGB benchmarks
- Graph-augmented LLM systems for intelligent reasoning and recommendation.
Each project walks you through data preprocessing, model architecture, training, evaluation, deployment, and monitoring-so you don't just learn concepts, you build real systems. Who This Book Is For
- Data scientists and ML engineers expanding into graph-based AI
- AI researchers exploring next-generation GNN and Transformer architectures
- Backend and platform engineers deploying graph intelligence at scale
- Professionals working with knowledge graphs, recommendation systems, and complex networks
A working knowledge of Python and basic machine learning is recommended. Why This Book Stands Out
Unlike fragmented tutorials or outdated references, Graph Machine Learning Mastery delivers a modern, unified, and production-focused roadmap-from classical graph learning to cutting-edge LLM-powered Graph AI. With deep technical insight, real-world case studies, and extensive appendices packed with APIs, cheat sheets, troubleshooting guides, and learning paths, this book is designed to become your long-term reference and career accelerator.
If you're serious about mastering Graph Machine Learning, Graph Transformers, Temporal GNNs, and LLM-driven AI systems, this is the book you've been waiting for.
