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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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

A* algorithm 77-79

versus Dijkstra’s algorithm 77

activation function 174

Adam optimizers 175

adjacency matrix 88

African Institute for Mathematical Sciences (AIMS) 37, 45

AIMS Cameroon student network epidemic model 58-63

algebraic connectivity 89

arcs 7

autoregressive integrated moving average models (ARIMA models) 103

autoregressive structure 235

B

Barabasi-Albert model 12

Bayesian inference criterion (BIC) 190

Bayesian networks 185

Bayes’ Theorem 183-185

BERT model 95

betweenness centrality 54, 105, 147, 238

biological networks 26-28

bnlearn analysis 189-193

Bridges 54

Burkina Faso market dataset 122, 123

C

causal pathways 185

ÄŒech complex 116

centrality measurements 101

centrality metrics...

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