Alan Julian Izenman (Philadelphia Temple University)

Network Models for Data Science

Theory, Algorithms, and Applications

• Gebundenes Buch

Produktbeschreibung

This book for graduate students in statistics, data science, computer science, machine learning, and mathematics explores the theory of complex networks, modern analysis methods, and computational issues. Applications range from technology and information to finance to social science to computational biology, physics, and engineering.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 550
• Erscheinungstermin: 5. Januar 2023
• Englisch
• Abmessung: 257mm x 177mm x 29mm
• Gewicht: 1154g
• ISBN-13: 9781108835763
• ISBN-10: 1108835767
• Artikelnr.: 63980479

Autorenporträt

Alan J. Izenman is Professor of Statistical Science at Temple University. He received his Ph.D. from the University of California, Berkeley. He was a faculty member at Tel Aviv University and Colorado State University, and was a visiting faculty member at the University of Chicago, the University of Minnesota, and Stanford University. He was Program Director of Statistics and Probability at NSF (1992-94). A Fellow of the ASA, RSS, and ISI, he has served on the Editorial Boards of JASA, Law, Probability, and Risk, and Statistical Analysis and Data Mining. He is the author of Modern Multivariate Statistical Techniques (2013).

Inhaltsangabe

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.