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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

I can’t believe we’ve made it to this point. At the beginning of this book, this felt like an impossible task, and yet here we are. In order to do the hands-on exercises for this chapter, we’ve used what we learned in the previous chapters. I hope I have shown you how networks can be useful, and how to work with them.

At the beginning of this book, I set out to write a practical hands-on book that would be code-heavy, not math-heavy. There are tons of network analysis books out there that have an emphasis on math but do not show actual implementation very well, or at all. I hope this book has effectively bridged the gap, giving a new skill to coders, and showing social scientists programmatic ways to take their network analysis to new heights. Thank you so much for reading this book!

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