Graph Machine Learning Mastery

Graph Machine Learning Mastery
A Complete Guide to Graph Neural Networks, Graph Transformers, Temporal GNNs, and LLM-Powered Graph AI with PyTorch Geometric & DGL

Graph-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 .

• 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.

• 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.

• 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