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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

Download a Free PDF Copy of This Book

Graph querying engine

In the last decade, due to the large diffusion of non-structured data, NoSQL databases have started to gain considerable attention and importance. Among them, graph databases are indeed extremely powerful for storing information based on a relation between entities. Indeed, in many applications, data can naturally be seen as entities, associated with metadata in the form of node properties, connected by edges that also have properties that further describe the relationship between entities.

Examples of graph databases are libraries or tools such as Neo4j, OrientDB, ArangoDB, Amazon Neptune, Cassandra, JanusGraph (previously named TitanDB), Google Spanner Graph, and Microsoft Cosmos DB. In the following sections, we will briefly describe some of them, together with the languages that allow us to query and traverse the underlying graphs, which are called graph querying languages.

Neo4j

At the time of writing, Neo4j (https://neo4j.com/) is one of the...

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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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