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Practical Data Science Cookbook, Second Edition

You're reading from   Practical Data Science Cookbook, Second Edition Data pre-processing, analysis and visualization using R and Python

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787129627
Length 434 pages
Edition 2nd Edition
Languages
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Authors (5):
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Anthony Ojeda Anthony Ojeda
Author Profile Icon Anthony Ojeda
Anthony Ojeda
Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
ABHIJIT DASGUPTA ABHIJIT DASGUPTA
Author Profile Icon ABHIJIT DASGUPTA
ABHIJIT DASGUPTA
Sean P Murphy Sean P Murphy
Author Profile Icon Sean P Murphy
Sean P Murphy
Bhushan Purushottam Joshi Bhushan Purushottam Joshi
Author Profile Icon Bhushan Purushottam Joshi
Bhushan Purushottam Joshi
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Toc

Table of Contents (12) Chapters Close

Preface 1. Preparing Your Data Science Environment FREE CHAPTER 2. Driving Visual Analysis with Automobile Data with R 3. Creating Application-Oriented Analyses Using Tax Data and Python 4. Modeling Stock Market Data 5. Visually Exploring Employment Data 6. Driving Visual Analyses with Automobile Data 7. Working with Social Graphs 8. Recommending Movies at Scale (Python) 9. Harvesting and Geolocating Twitter Data (Python) 10. Forecasting New Zealand Overseas Visitors 11. German Credit Data Analysis

Finding key players


In the previous recipe, Finding strong ties, we began exploring ego networks and strong ties between individuals in our social network. We started to see that actors with strong ties with other actors created clusters that centered on themselves. This leads to the obvious question: who are the key figures in the graph, and what kind of pull do they have? We'll look at a couple of measures to determine how important a node is or its centrality to try to discover the degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality.

Getting ready

If you completed the previous recipes, you will be ready to start this one.

How to do it...

The following steps will identify key players in this network of comic book characters:

  1. To find the top ten nodes in the heroes network, we compute the nodes' degree and sort them:
import operator

>>> degrees = sorted(graph.degree().items(), key=operator.itemgetter(1), reverse=True)

>>> for node in...
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