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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

Model insights

To me, the model insights are more exciting than building and using the models for prediction. I enjoy learning about the world around me, and ML models (and networks) allow me to understand the world in a way that my eyes do not. We cannot see all of the lines that connect us as people, and we cannot easily understand influence based on how the people around us are strategically placed in the social networks that exist in real life. These models can help with that! Networks can provide the structure to extract contextual awareness of information flow and influence. ML can tell us which of these pieces of information is most useful in understanding something. Sometimes, ML can cut right through the noise and get right to the signals that affect our lives.

With the model that we just built, one insight is that the book Les Miserables has different characters by type in different network structures. Revolutionaries are close to each other and tightly connected. Students...

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