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Graph Machine Learning

You're reading from Graph Machine Learning Learn about the latest advancements in graph data to build robust machine learning models

Product type Paperback

Published in Jul 2025

Publisher Packt

ISBN-13 9781803248066

Length 434 pages

Edition 2nd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Authors (3):

Aldo Marzullo

Enrico Deusebio

Claudio Stamile

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Table of Contents (20) Chapters

Preface

1. Part 1: Introduction to Graph Machine Learning

2. Getting Started with Graphs FREE CHAPTER

3. Graph Machine Learning

4. Neural Networks and Graphs

5. Part 2: Machine Learning on Graphs

6. Unsupervised Graph Learning

7. Supervised Graph Learning

8. Solving Common Graph-Based Machine Learning Problems

9. Part 3: Practical Applications of Graph Machine Learning

10. Social Network Graphs

11. Text Analytics and Natural Language Processing Using Graphs

12. Graph Analysis for Credit Card Transactions

13. Building a Data-Driven Graph-Powered Application

14. Part 4: Advanced topics in Graph Machine Learning

15. Temporal Graph Machine Learning

16. GraphML and LLMs

17. Novel Trends on Graphs

18. Index

19. Other Books You May Enjoy

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Summary

In this section, we have provided you with the basic concepts of how to design, implement, and deploy data-driven applications that resort to graph modeling and leverage graph structures. We have highlighted the importance of a modular approach, which is usually the key to seamlessly scaling any data-driven use case from early-stage MVPs to production systems that can handle a large amount of data and large computational performances.

We have outlined the main architectural pattern, which should provide you with a guide when designing the backbone structure of your data-driven applications. We then continued by describing the main components that are the basis of graph-powered applications: graph processing engines, graph databases, and graph querying languages. For each component, we have provided an overview of the most common tools and libraries, with practical examples that will help you to build and implement your solutions. You should thus have by now a good overview...

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Authors (3)

Aldo Marzullo

Aldo Marzullo

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.

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Enrico Deusebio

Enrico Deusebio

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.

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Claudio Stamile

Claudio Stamile

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.

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