Graph Machine Learning Essentials

Graph Machine Learning Essentials delivers a practical and technically grounded introduction to modern Graph ML, effectively connecting foundational graph concepts with real-world AI implementation workflows.

-- Lucas Cabral,

AI Engineer & Data Scientist

Graph Machine Learning Essentials is a compact, practical guide for engineers who want a quick start in Graph ML, covering key methods, tasks, applications, and implementation pathways. It is a valuable handbook for navigating modern graph ML concepts and real-world pipelines.

-- Dymitr Nowicki,

Ph.D. in Computer Science and Applied Mathematics,

Selecton Technologies Inc.

A comprehensive and accessible introduction to the burgeoning field of graph machine learning (GML). Aimed at readers with a basic understanding of machine learning, the book expertly balances theory and practice, making it suitable for students, professionals, and researchers alike.

The book begins by introducing graphs as structures that model relationships between entities, highlighting their ubiquity in domains like social networks, biology, and finance. Pintu explains why traditional machine learning methods fall short for graph-structured data, setting the stage for specialized techniques like node embeddings and Graph Neural Networks (GNNs). Each chapter builds logically on the last, covering core tasks (node classification, edge prediction, graph classification), advanced architectures, and practical considerations like scalability and over-smoothing.

What sets the book apart is its practical focus. Pintu includes code snippets, programming assignments, and discussions on real-world applications-such as fraud detection, recommender systems, and drug discovery-to ensure readers can apply what they learn. The use of quizzes and examples further reinforces understanding, while appendices on machine learning basics and PyTorch Geometric make the book self-contained.

Graph Machine Learning Essentials is an invaluable guide for anyone looking to understand and apply GML, offering both the theoretical foundations and the practical tools needed to harness the power of graph-structured data.

-- Wilson Yeung,

Reviewer