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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
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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 took raw data, performed a series of steps to clean the network, and even carried out a very simple attack simulation.

I hope that at this point, looking at and working with networks is starting to feel more natural. The more that I work with networks, the more that I see them in everything, and they affect my understanding of the world. We are using a fantasy story for this chapter because it is of a manageable size to explain the construction, cleaning, and some simple analysis. As you learn more about networks, you will likely find that real-world networks are usually much messier, more complicated, and larger. I hope that this simple network will give you the tools and practice you need to eventually chase much more ambitious problems.

In the next chapter, we’re going to have a lot of fun. Our next chapter is about analyzing whole networks. You will learn all kinds of useful things, such as how to identify the most influential nodes in a...

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