“Very few people write about using network graphs at this level. This book fuses two of my favourite topics: graph analysis and machine learning and is truly one of the most impressive network/graph books that I own. It also brings attention to the cool work happening with Karate Club, one of the most interesting Python libraries around. I loved reading the First Edition of this book, and I read it while I was writing my own book, which inspired me to learn more and work harder. My copy is filled with highlighter pen marks and notes written on the pages.

If you love working with graphs or are curious to learn how, you need to read this book. It is essential, and there is nothing quite like it. Thank you to the authors and contributors for creating this book and for continuing with the Second Edition.”

David Knickerbocker, Chief Data Scientist and Founder, Hometree Data

“Graph Machine Learning offers a well-structured view of how important graph data is in today's AI. It moves smoothly from basic concepts and spectral insights to advanced topics like graph neural networks, attention mechanisms, and contrastive learning strategies. The book is further bolstered by practical Python examples and real-world best practices for scalability and robustness.”

Etibar Aliyev, Member of AI for Developing Countries Forum

“In 2025's AI landscape, graph machine learning separates market leaders from followers. Major tech companies have already deployed GNNs for competitive advantage—LinkedIn's job matching, Amazon's fraud detection, Pinterest's 3-billion-node recommendation engine.

This book provides the foundational knowledge and practical tools to implement similar graph ML solutions, showing how graph structures unlock business value that traditional ML cannot capture using an end-to-end machine learning pipeline approach.

The authors balance technical depth with business pragmatism, making this essential reading for any leader building data-driven competitive moats. If your organization handles networked data—customers, products, transactions—this book shows how to monetize those relationships.”

Pierre-Andre Galmes, Product Director, ex-PayPal, Riot Games, ZipRecruiter

Aldo Marzullo received an M.Sc. degree in computer science from the University of Calabria (Cosenza, Italy) in September 2016. During his studies, he developed a solid background in several areas, including algorithm design, graph theory, and machine learning. In January 2020, he received his joint Ph.D. from the University of Calabria and Université Claude Bernard Lyon 1 (Lyon, France), with a thesis titled Deep Learning and Graph Theory for Brain Connectivity Analysis in Multiple Sclerosis. He is currently a postdoctoral researcher and collaborates with several international institutions.

Enrico Deusebio is currently working as engineering manager at Canonical, the publisher of Ubuntu, to promote open source technologies in the data and AI space and to make them more accessible to everyone. He has been working with data and distributed computing for over 15 years, both in an academic and industrial context, helping organizations implement data-driven strategies and build AI-powered solutions. He has collaborated and worked with top-tier universities, such as the University of Cambridge, University of Turin, and the Royal Institute of Technology (KTH) in Stockholm, where he obtained a Ph.D. in 2014. He holds a B.Sc. and an M.Sc. degree in aerospace engineering from Politecnico di Torino.

Claudio Stamile received an M.Sc. degree in computer science from the University of Calabria (Cosenza, Italy) in September 2013 and, in September 2017, he received his joint Ph.D. from KU Leuven (Leuven, Belgium) and Université Claude Bernard Lyon 1 (Lyon, France). During his career, he developed a solid background in AI, graph theory and machine learning with a focus on the biomedical field.