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Network Science with Python

You're reading from   Network Science with Python Explore the networks around us using network science, social network analysis, and machine learning

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Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781801073691
Length 414 pages
Edition 1st Edition
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Author (1):
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David Knickerbocker David Knickerbocker
Author Profile Icon David Knickerbocker
David Knickerbocker
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Getting Started with Natural Language Processing and Networks
2. Chapter 1: Introducing Natural Language Processing FREE CHAPTER 3. Chapter 2: Network Analysis 4. Chapter 3: Useful Python Libraries 5. Part 2: Graph Construction and Cleanup
6. Chapter 4: NLP and Network Synergy 7. Chapter 5: Even Easier Scraping! 8. Chapter 6: Graph Construction and Cleaning 9. Part 3: Network Science and Social Network Analysis
10. Chapter 7: Whole Network Analysis 11. Chapter 8: Egocentric Network Analysis 12. Chapter 9: Community Detection 13. Chapter 10: Supervised Machine Learning on Network Data 14. Chapter 11: Unsupervised Machine Learning on Network Data 15. Index 16. Other Books You May Enjoy

Network Analysis

In this chapter, I am going to describe three different topics, but at a very high level: graph theory, social network analysis, and network science. We will begin by discussing some of the confusion around the word network and why that will probably remain confusing. Then, we will go over a tiny bit of the past and present of all three. Finally, we will dive into how network analysis has helped me and hopefully how it can help you. This isn’t a code-heavy chapter. This is a high-level introduction.

The following topics will be discussed in this chapter:

  • The confusion behind networks
  • What is this network stuff?
  • Resources for learning network analysis
  • Common network use cases
  • Advanced network use cases
  • Getting started with networks
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