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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

Neural network architectures as graphs

In Chapter 1, we touched on deep learning models, particularly in the context of generative artificial intelligence. Deep learning models surface in many areas of analytics, including natural language processing, image classification, and time series forecasting. Let’s explore these applications in more detail.

Natural language processing is ubiquitous in the era of big data. Surveys often employ free text collection methods. Customers provide text reviews of products. Social scientists jot down notes when they are observing populations qualitatively (dubbed ethnographic research). Bloggers post regular content to disseminate ideas. Legal, medical, and educational notes can include biased content that needs to be addressed to protect people from institutional bias that limits future opportunities and wellness.

All this data needs to be parsed before it is fed into a classification model or exploratory data tools. Most of the tools...

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