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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

View More author details

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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Technical requirements

All code files relevant to this chapter are available at https://github.com/PacktPublishing/Graph-Machine-Learning/tree/main/Chapter01. Please refer to the Practical exercises section for guidance on how to set up the environment to run the examples in this chapter, either using Poetry, pip, or Docker.

For more complex data visualization tasks provided in this chapter, Gephi (https://gephi.org/) may also be required. The installation manual is available here: https://gephi.org/users/install/.https://gephi.org/users/install/.

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Graph Machine Learning

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Graph Machine Learning - Second Edition

Published in: Jul 2025

Publisher: Packt

ISBN-13: 9781803248066

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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 entitled Deep Learning and Graph Theory for Brain Connectivity Analysis in Multiple Sclerosis. He is currently a postdoctoral researcher at the University of Calabria and collaborates with several international institutions.

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Enrico Deusebio

Enrico Deusebio

Enrico Deusebio is currently the chief operating officer at CGnal, a consulting firm that helps its top-tier clients implement data-driven strategies and build AI-powered solutions. He has been working with data and large-scale simulations using high-performance facilities and large-scale computing centers for over 10 years, both in an academic and industrial context. He has collaborated and worked with top-tier universities, such as the University of Cambridge, the University of Turin, and the Royal Institute of Technology (KTH) in Stockholm, where he obtained a Ph.D. in 2014. He also holds B.Sc. and M.Sc. degrees 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 has developed a solid background in artificial intelligence, graph theory, and machine learning, with a focus on the biomedical field. He is currently a senior data scientist in CGnal, a consulting firm fully committed to helping its top-tier clients implement data-driven strategies and build AI-powered solutions to promote efficiency and support new business models.

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