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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

Max-flow min-cut algorithm

Aside from shortest paths and routes, transportation logistics sometimes involve city planning to plan, say, roadwork with the least interruption to traffic patterns or supply chains. The goal is to maximize traffic flow through points of interest (say, major intersections or buildings with high volumes of visitors/workers each day) while minimizing which routes are cut off.

In graph theory, the max-flow min-cut algorithm seeks to partition a network to maximize the flow of information through a social network, the flow of traffic in a transportation network, or the flow of material through an electrical or water pipeline network, among others. Typically, there’s a starting vertex and an ending vertex with respect to flow, though it is possible to run the algorithm through all possible combinations and aggregate results to maximize flow for the entire network.

Let’s consider the example of traffic flow from a dense residential area outside...

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