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Graph Machine Learning

You're reading from   Graph Machine Learning Take graph data to the next level by applying machine learning techniques and algorithms

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Product type Paperback
Published in Jun 2021
Publisher Packt
ISBN-13 9781800204492
Length 338 pages
Edition 1st Edition
Languages
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Authors (3):
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Aldo Marzullo Aldo Marzullo
Author Profile Icon Aldo Marzullo
Aldo Marzullo
Claudio Stamile Claudio Stamile
Author Profile Icon Claudio Stamile
Claudio Stamile
Enrico Deusebio Enrico Deusebio
Author Profile Icon Enrico Deusebio
Enrico Deusebio
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1 – Introduction to Graph Machine Learning
2. Chapter 1: Getting Started with Graphs FREE CHAPTER 3. Chapter 2: Graph Machine Learning 4. Section 2 – Machine Learning on Graphs
5. Chapter 3: Unsupervised Graph Learning 6. Chapter 4: Supervised Graph Learning 7. Chapter 5: Problems with Machine Learning on Graphs 8. Section 3 – Advanced Applications of Graph Machine Learning
9. Chapter 6: Social Network Graphs 10. Chapter 7: Text Analytics and Natural Language Processing Using Graphs 11. Chapter 8:Graph Analysis for Credit Card Transactions 12. Chapter 9: Building a Data-Driven Graph-Powered Application 13. Chapter 10: Novel Trends on Graphs 14. Other Books You May Enjoy

Chapter 6: Social Network Graphs

The growth of social networking sites has been one of the most active trends in digital media over the 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 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, enables a powerful tool to extract useful knowledge.

However, understanding the dynamics...

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