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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

Challenge yourself!

Before concluding this chapter, I want to bring a challenge forward to you. You have learned how to create networks using text, what an edge list looks like and how to build one in pandas, how to create a network, and how to clean a network, and now you have an introduction to whole network analysis. You now have every tool required to start your journey into network analysis. I will explain how to do much more in later chapters, but you have all the tools you need to get started and get hooked on network analysis.

I want to challenge you to think about your own data, about the data you work with, about the social networks you play on, about the work networks you collaborate in, and more. I want you to consider how you could take these living networks, describe them in an edge list (it’s just source and target columns), render a network visualization, and analyze the networks. You have the tools to do this, and networks are all around us. I recommend that...

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