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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

Preface

Hello there! Network science combines the power of analytics with the deep theoretical tools of graph theory to solve difficult problems in data analytics. This empowers researchers and industry engineers/data scientists to analyze data at scale and reframe intractable analytics problems to produce powerful insights into problems and predictions about system behaviors, including biological, physical, and social systems of interest.

There are many important applications of network science today, including these:

  • Social network data
  • Spatial data
  • Time series data
  • Spatiotemporal data
  • More advanced data structures, such as ontologies or hypergraphs

This book gives a brief overview of social network applications and focuses on the cutting edge of network science applications to areas of data science, such as transportation logistics, conversation, public health, linguistics, and education. By the end of your journey, you’ll be able to frame your own data problem within the framework of network science to derive insights and tackle difficult problems in your field.

We will provide the necessary mathematical background as we dive into practical examples and code related to our work in academia and industry over the past decades, including work on predicting Ebola outbreaks, forecasting food price volatility, modeling genetic and linguistic relationships, and mining social networks for insights into social tie formation. As the world faces food shortages, public health crises, economic inequality, supply chain breakdowns, and environmental crises, network science will play an important role in big data analytics for social good.

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