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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

Product type Paperback

Published in Jun 2024

Publisher Packt

ISBN-13 9781805127895

Length 290 pages

Edition 1st Edition

Languages

Python

Concepts

Statistics

Authors (2):

Franck Kalala Mutombo

Colleen M. Farrelly

View More author details

Table of Contents (21) Chapters

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

Why subscribe?

20. Other Books You May Enjoy

References

Gladwin, T. E., Figner, B., Crone, E. A., & Wiers, R. W. (2011). Addiction, adolescence, and the integration of control and motivation. Developmental cognitive neuroscience, 1(4), 364-376.

Heckerman, D. (2008). A tutorial on learning with Bayesian networks. Innovations in Bayesian networks: Theory and applications, 33-82.

Hoffman, K. I. (1993). The USMLE, the NBME subject examinations, and assessment of individual academic achievement. Academic Medicine, 68(10), 740-7.

Hoyle, R. H. (Ed.). (1995). Structural equation modeling: Concepts, issues, and applications. Sage.

Igolkina, A. A., & Meshcheryakov, G. (2020). semopy: A Python package for structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 27(6), 952-963.

Kaufman, K. A., LaSalle-Ricci, V. H., Glass, C. R., & Arnkoff, D. B. (2007). Passing the bar exam: Psychological, educational, and demographic predictors of success. J. Legal Educ., 57, 205.

Mak, K. K....

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Authors (2)

Colleen M. Farrelly

Colleen M. Farrelly

Colleen M. Farrelly is a lead data scientist and researcher with a broad industry background in machine learning algorithms and domains of application. While her focus has been industry, she also publishes academically in geometry, network science, and natural language processing. Colleen earned a graduate degree in Biostatistics from the University of Miami. Her work history includes fields like nuclear engineering, public health, biotechnology, retail, educational technology, and human behavior analytics. She previously published The Shape of Data, a comprehensive overview of machine learning from a geometric perspective. Colleen is currently focused on applications of generative models and tech education in the developing world

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Franck Kalala Mutombo

Franck Kalala Mutombo

Franck Kalala Mutombo is a Professor of Mathematics at Lubumbashi University and former Academic Director of AIMS-Senegal. He previously worked in a research position at Strathclyde University and at AIMS-South Africa in a joint appointment with the University of Cape Town. He holds a PhD in Mathematical Sciences (with focus in network science) from the University of Strathclyde, Glasgow, Scotland. His current research considers the impact of network structure on long-range interactions applied to epidemics, diffusion, object clustering, differential geometry of manifolds, finite element methods for PDEs, and data science. Currently, he teaches at University of Lubumbashi and across the AIMS Network.

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