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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? FREE CHAPTER 2. Working with Networks in NetworkX 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

Biadjacency matrices

Bipartite graphs can be represented using another type of matrix. Bipartite graphs have two types of vertices, which I'll call row-vertices and column-vertices, for reasons that will become obvious. All edges connect one row-vertex to one column-vertex, so it's not necessary to use a full adjacency matrix connecting all possible vertex pairs. Instead, we represent the edge from the ith row-vertex to the jth column-vertex by setting the element of the matrix at row i and column j. This type of matrix is called a biadjacency matrix, and is typically denoted as B. Because the number of row vertices and column vertices can be different, the biadjacency matrix does not need to be square. The bipartite graph can be projected into a graph containing only row-nodes (or only column-nodes) by using simple matrix operations.

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