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Network Science with Python and NetworkX Quick Start Guide

You're reading from   Network Science with Python and NetworkX Quick Start Guide Explore and visualize network data effectively

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789955316
Length 190 pages
Edition 1st Edition
Languages
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Author (1):
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Edward L. Platt Edward L. Platt
Author Profile Icon Edward L. Platt
Edward L. Platt
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Table of Contents (15) Chapters Close

Preface 1. What is a Network? 2. Working with Networks in NetworkX FREE CHAPTER 3. From Data to Networks 4. Affiliation Networks 5. The Small Scale - Nodes and Centrality 6. The Big Picture - Describing Networks 7. In-Between - Communities 8. Social Networks and Going Viral 9. Simulation and Analysis 10. Networks in Space and Time 11. Visualization 12. Conclusion 13. Other Books You May Enjoy Appendix

Hubs – eigenvector centrality

Imagine having an important message that needs to reach an entire group (for example, your employer or school), but only being able to give that message to one person. Who would you tell? You'd want to find someone well-connected to the entire network. You might try the person with the highest degree centrality (the most friends). The downside to that approach is that their friends might not be well connected to the rest of the network. In a hypothetical company, for example, the Director of East Coast Sales might know the most people, but might not know how to reach anyone in other departments or regions. Instead, it would be better to find someone who is highly connected to other highly-connected people, such as the CEO (or, more likely, their assistant). Such individuals are sometimes called hubs, because, like the center of a spoked...

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