Graph Machine Learning Essentials: Foundations, Hands-On Implementation, Graph Neural Networks, PyTorch Geometric, and Applied Use Cases (Self-Learning Management Series)

Foundations, Hands-On Implementation, Graph Neural Networks, PyTorch Geometric, and Applied Use Cases. Introduction to graph machine learning, hands-on implementations, and applied use cases.

Learn Graph Machine Learning, Graph Neural Networks, and PyTorch Geometric in One Practical Guide
Graph Machine Learning Essentials is a structured and easy-to-follow guide for ML engineers, data scientists, researchers, and technology professionals who want to understand how machine learning works on graph-structured data. From graph fundamentals and node embeddings to message passing, GNN architectures, and real-world applications, this book helps readers build practical knowledge they can use with confidence.

Key Features of the Book

• Strong foundations in graph machine learning – Understand graphs, graph learning tasks, node embeddings, message passing, and graph neural network basics in simple language.
• Advanced GNN concepts made approachable – Explore modern architectures and practical issues such as scalability, oversmoothing, and over-squashing without feeling overwhelmed.
• Real-world applications across industries – See how graph machine learning can support social network analysis, recommender systems, fraud detection, cybersecurity, bioinformatics, and knowledge graphs.
• Built for learning and retention – Reinforce concepts with end-chapter quizzes, in-chapter QR-based questions, practical examples, and programming assignments as online resources.

This book is designed to help readers not only understand graph machine learning but also apply it to real-world data problems. Whether you are working with connected data in finance, recommendation systems, cybersecurity, biology, or AI research, this guide gives you a practical path forward.

You will learn how to represent data as graphs, choose the right graph learning task, implement graph neural networks, and handle common challenges that arise with large and complex graphs.

If you want a beginner-friendly yet well-structured introduction to graph machine learning, this book will help you build the confidence to start working with graph data and graph neural networks in practice.

Pintu Kumar is a Ph.D. scholar at IIT Bombay specializing in graph machine learning. A PMRF fellow and Silver Medalist in Mathematics, he focuses on simplifying complex ideas.

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