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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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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

Hypergraphs

Another exciting avenue of research in network science involves hypergraphs, which are an extension of networks in which multi-way relationships can include multiple vertices. In this way, the concept of edges is extended in a similar way to how the concept of edges was extended in Chapter 6, within the Extending network metrics for time series analytics section on simplicial complexes. In fact, the abstraction of simplicial complexes is one type of hypergraph.

Hypergraphs are often useful for visualizing database diagrams and planning information retrieval systems, where many-to-many relationships often exist. Some distributed computing systems even provide hypergraph algorithms to aid in these retrieval tasks at scale.

Visualization via hypergraphs is also used extensively in bioinformatics, where multi-way relationships exist between proteins, biological pathways, gene expression datasets, and metabolic processes. In complex systems, it is often easier to show...

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