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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

Summary

In this chapter, we learned how to find and scrape raw text, convert it into an entity list, and then convert that entity list into an actual social network so that we can investigate revealed entities and relationships. Did we capture the who, what, and where of a piece of text? Absolutely. I hope you can now understand the usefulness of NLP and social network analysis when used together.

In this chapter, I showed several ways to get data. If you are not familiar with web scraping, then this might seem a bit overwhelming, but it’s not so bad once you get started. However, in the next chapter, I will show several easier ways to get data.

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