Graph Machine Learning Mastery (Paperback)

Philip Oscar

ISBN 13: 9798261760221

Published by Independently Published, 2025

About this Item

Paperback. Graph Machine Learning MasteryA 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 LearnBuild powerful GNN architectures: GCN, GAT, GraphSAGE, GIN, heterogeneous and large-scale GNNsTransition from GNNs to Graph Transformers with positional encodings and attention mechanismsModel temporal and dynamic graphs using TGN, TGAT, DySAT, and continuous-time message passingDesign LLM + GNN hybrid systems for reasoning, knowledge graphs, and GraphRAG pipelinesApply graph ML to real-world domains: fraud detection, recommender systems, molecular graphs, finance, telecom, and cybersecurityTrain, optimize, monitor, and deploy graph models in production environmentsIntegrate 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 predictionTemporal graph forecastingMolecular property prediction with OGB benchmarksGraph-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 ForData scientists and ML engineers expanding into graph-based AIAI researchers exploring next-generation GNN and Transformer architecturesBackend and platform engineers deploying graph intelligence at scaleProfessionals working with knowledge graphs, recommendation systems, and complex networksA 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9798261760221

Bibliographic Details

• Title: Graph Machine Learning Mastery (Paperback)
• Publisher: Independently Published
• Publication Date: 2025
• Language: English
• ISBN 13: 9798261760221
• Binding: Paperback
• Condition: new

About this title

Synopsis

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 .

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.