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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
Languages
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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “This script shows that average subgraph centrality varies between the two subfamily trees, with Greenberg’s average subgraph centrality of 2.478 and Dimmendaal’s average subgraph centrality of 3.276.”

A block of code is set as follows:

#compare subgraph centrality of language families
gs=nx.subgraph_centrality(G)
print(np.mean(np.array(list(gs.values()))))
gs2=nx.subgraph_centrality(G2)
print(np.mean(np.array(list(gs2.values()))))

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “When you hover over the Movie DBMS label on the right-hand side of the screen, you’ll see a Start button that launches the connection to this database. Click on Start.”

Tips or important notes

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