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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

Transportation Data

This chapter tackles transportation logistics, which involves the movement of supplies or goods from one location to another. We’ll introduce a goods delivery problem to find the optimal routing of supplies to minimize the delivery time and cost to deliver the goods. We’ll explore shortest paths, optimal routes to visit all necessary locations, and scaling algorithms to large networks. Further, we’ll examine caveats to simple distance weightings to calculate route optimality, considering delivery hazards on routes that can influence optimality.

When you have finished this chapter, you’ll understand how to frame transportation problems as network problems and scale them to very large routing networks using Python.

Specifically, we will cover the following topics in this chapter:

  • Introduction to transportation problems
  • Shortest path applications
  • Traveling salesman problem
  • Maximum flow/minimum cut (max-flow min...
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