Network Models for Data Science
Theory, Algorithms, and Applications

This is the first book to describe modern methods for analyzing complex networks arising from a wide range of disciplines.

Alan Julian Izenman (Author)

9781108835763, Cambridge University Press

Hardback, published 5 January 2023

550 pages
25.9 x 18.5 x 2.8 cm, 1.14 kg

This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component.

Preface
1. Introduction and preview
2. Examples of networks
3. Graphs and networks
4. Random graph models
5. Percolation on Zd
6. Percolation beyond Zd
7. The topology of networks
8. Models of network evolution and growth
9. Network sampling
10. Parametric network models
11. Graph partitioning: i. graph cuts
12. Graph partitioning: ii. community detection
13. Graph partitioning: iii. spectral clustering
14. Graph partitioning: iv. overlapping communities
15. Examining network properties
16. Graphons as limits of networks
17. Dynamic networks
Index of examples
Author index
Subject index.

Subject Areas: Probability & statistics [PBT]