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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

Why cover Requests and BeautifulSoup?

We all like it when things are easy, but life is challenging, and things don’t always work out the way we want. In scraping, that’s laughably common. Initially, you can count on more things going wrong than going right, but if you are persistent and know your options, you will eventually get the data that you want.

In the previous chapter, we covered the Requests Python library, because this gives you the ability to access and use any publicly available web data. You get a lot of freedom when working with Requests. This gives you the data, but making the data useful is difficult and time-consuming. We then used BeautifulSoup, because it is a rich library for dealing with HTML. With BeautifulSoup, you can be more specific about the kinds of data you extract and use from a web resource. For instance, we can easily harvest all of the external links from a website, or even the full text of a website, excluding all HTML.

However...

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