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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

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Product type Paperback
Published in Jul 2025
Publisher Packt
ISBN-13 9781803248066
Length 434 pages
Edition 2nd Edition
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Authors (3):
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Aldo Marzullo Aldo Marzullo
Author Profile Icon Aldo Marzullo
Aldo Marzullo
Enrico Deusebio Enrico Deusebio
Author Profile Icon Enrico Deusebio
Enrico Deusebio
Claudio Stamile Claudio Stamile
Author Profile Icon Claudio Stamile
Claudio Stamile
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Toc

Table of Contents (20) Chapters Close

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

Social Network Graphs

The growth of social networking sites has been one of the most active trends in digital media over recent years. Since the late 1990s, when the first social applications were published, they have attracted billions of active users worldwide, many of whom have integrated digital social interactions into their daily lives. New ways of communication are being driven by social networks such as Facebook, Twitter, and Instagram, among others. Users can share ideas, post updates and feedback, or engage in activities and events while sharing their broader interests on social networking sites.

Besides, social networks constitute a huge source of information for studying user behaviors, interpreting the interaction among people, and predicting their interests. Structuring them as graphs, where a vertex corresponds to a person and an edge represents the connection between them, provides a powerful tool to extract useful knowledge.

However, understanding the dynamics...

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