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Modern Graph Theory Algorithms with Python

You're reading from   Modern Graph Theory Algorithms with Python Harness the power of graph algorithms and real-world network applications using Python

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781805127895
Length 290 pages
Edition 1st Edition
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Concepts
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Authors (2):
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Franck Kalala Mutombo Franck Kalala Mutombo
Author Profile Icon Franck Kalala Mutombo
Franck Kalala Mutombo
Colleen M. Farrelly Colleen M. Farrelly
Author Profile Icon Colleen M. Farrelly
Colleen M. Farrelly
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Toc

Table of Contents (21) Chapters Close

Preface 1. Part 1:Introduction to Graphs and Networks with Examples FREE CHAPTER
2. Chapter 1: What is a Network? 3. Chapter 2: Wrangling Data into Networks with NetworkX and igraph 4. Part 2: Spatial Data Applications
5. Chapter 3: Demographic Data 6. Chapter 4: Transportation Data 7. Chapter 5: Ecological Data 8. Part 3: Temporal Data Applications
9. Chapter 6: Stock Market Data 10. Chapter 7: Goods Prices/Sales Data 11. Chapter 8: Dynamic Social Networks 12. Part 4: Advanced Applications
13. Chapter 9: Machine Learning for Networks 14. Chapter 10: Pathway Mining 15. Chapter 11: Mapping Language Families – an Ontological Approach 16. Chapter 12: Graph Databases 17. Chapter 13: Putting It All Together 18. Chapter 14: New Frontiers 19. Index 20. Other Books You May Enjoy

Spectral graph tools

The adjacency matrix and degree of vertices in the adjacency matrix contain information about the connectivity of vertices within the network. For an undirected network, the Laplacian matrix of the network is found by subtracting the adjacency matrix from the degree matrix (i.e., L = D - A, where D is the degree matrix and A is the adjacency matrix). This matrix contains information about many important properties of the network, including the number of connected components, the sparsest cut that can separate the network into separate components, and the strength of connection within the network overall.

Connectivity is an important concept in graph theory. A connected network is one in which there is a path between all pairs of vertices; a disconnected network is one in which some vertices do not have paths to all other vertices. Connected networks can be separated by cutting edges; the minimum cut set is the set of the fewest edges that need to be removed...

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