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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

Simulating an attack

We already did an end-to-end workflow of converting a rough network edge list into a network, cleaning the network, and then persisting the cleaned network’s edge list, so for the remainder of this chapter, let’s do a simulation.

In most networks, some nodes serve as key hubs. These nodes reveal themselves if you look for the number of degrees (edges) that a node has, or by checking PageRank or various centrality metrics for nodes. We will use these approaches in a later chapter to identify important notes. For now, we have domain knowledge that we can use. Those of us who know this story can likely name by heart several of the important protagonists of the story: Alice, Mad Hatter, Cheshire Cat, and so on. And those of us who are familiar with the story are also likely very aware of the Queen of Hearts repeatedly shouting “OFF WITH THEIR HEADS!”

In a network, if you remove the most connected and important nodes, what often happens...

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