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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

Removing edges

There will likely be times when you will need to remove edges. This can be useful, not just for cleaning networks but also for simulating attacks, or for identifying cliques and communities. For instance, I often use what is called minimum cuts or minimum edge cuts to find the fewest number of edges that will split a network into two pieces. I use this for community detection, and also to spot emerging trends on social media.

With the Alice in Wonderland network, there are no edges that we need to remove, so I will first show you how to remove some edges, and then I’ll show you how to put them back:

  • You can remove edges one at a time:
    G.remove_edge('Dormouse', 'Tillie')
  • Alternatively, you can remove several at a time:
    drop_edges = [('Dormouse', 'Tillie'), ('Dormouse', 'Elsie'), ('Dormouse', 'Lacie')]
    G.remove_edges_from(drop_edges)

How does this look when visualized...

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