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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

Affiliation networks in NetworkX

Affiliation networks are represented in NetworkX using the same classes as other networks: Graph and DiGraph. The only difference is that you need to keep track of which nodes are of which type. In NetworkX, this is done by using a container (list, set, and so on) to store all node IDs for one node type. For convenience, the node type can also be stored as a node attribute. A network can be tested for an affiliation network structure using the sets() function. Usually, this function can also find the nodes of each type (the exception being when the network has groups of nodes that are not connected to the rest of the network). The following example loads our dear old friend from Chapter 2, Working with Networks in NetworkX, the Zachary karate club network, and checks it for affiliation network structure:

# Import bipartite module
from networkx.algorithms...
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