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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

Part 3: Network Science and Social Network Analysis

In these chapters, we learn how to analyze networks and hunt for insights. We begin with a discussion on whole network analysis and gradually zoom in to the node level, to investigate egocentric networks. We then look for communities and subgroups that exist in networks. Finally, we conclude the book by showing how graph data can be useful for supervised and unsupervised machine learning.

This section includes the following chapters:

  • Chapter 7, Whole Network Analysis
  • Chapter 8, Egocentric Network Analysis
  • Chapter 9, Community Detection
  • Chapter 10, Supervised Machine Learning on Network Data
  • Chapter 11, Unsupervised Machine Learning on Network Data
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