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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

Adjacency matrices

A matrix is a way of describing pairwise relationships. A matrix looks like a grid of numbers, as in the following example:

┌           ┐
│ 0 1 42 │
│ 0.5 -3 1 │
└ ┘

The preceding matrix contains six entries, organized in two rows and three columns. A matrix can have any number of rows or columns, but they are always rectangular. A matrix with two rows and three columns is described as a 2 x 3 matrix. If the entire matrix is called A, then the element at row i and column j is called Ai,j. So, in the preceding example, A2,1 = 0.5.

One way to represent a graph as a matrix is to place the weight of each edge in one element of the matrix (or a zero if there is no edge). So, an edge from v3, to v1 with a weight of 37 would be represented by A3,1 = 37, meaning the third row has a 37 in the first column...

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