Stochastic Actor-Oriented Models for Longitudinal Networks
Will be published approx. on 31. October 2026
Book
Hardback
500 pages
978-1-009-84367-6 (ISBN)
Description
Interest in social networks - patterns of relations between social actors such as individuals, corporations, and countries - has grown in the last decade, and analysis of longitudinal network data has moved forward strongly. Social networks often change; understanding this process, where changes lead to other changes, requires tools that can uncover the rules driving these changes. In 'Stochastic Actor-Oriented Models for Longitudinal Networks,' Tom A. B. Snijders and Christian Steglich bring together the first comprehensive textbook on the Stochastic Actor-Oriented Model (SAOM), a leading method for analyzing dynamic network data. They present the diverse SAOM variants developed over the past three decades, covering the co-evolution of networks and actor attributes as well as the co-evolution of multiple one-mode and two-mode networks. Providing a foundation for applying the methods as well as advice for problems encountered in practice, this book offers a detailed guide into the best practices of modeling longitudinal network data.
More details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Illustrations
Worked examples or Exercises
ISBN-13
978-1-009-84367-6 (9781009843676)
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Schweitzer Classification
Other editions
Additional editions
Tom A.B. Snijders | Christian Steglich
Stochastic Actor-Oriented Models for Longitudinal Networks
Book
approx. 10/2026
Cambridge University Press
€61.00
Not yet published
Persons
Tom A. B. Snijders is Emeritus Professor at Oxford and Groningen. His research focuses on methods for social network and multilevel analysis. He initiated the Stochastic Actor-oriented Model, 'SIENA.' He obtained honorary doctorates from the universities of Stockholm and Paris Dauphine, and the Paul L. Lazarsfeld Award from the American Sociological Association. Christian Steglich is Associate Professor of sociology at the University of Groningen and the Institute for Analytical Sociology, Linkoeping University. His research focuses on statistical inference for social networks, social influence, and modeling macro- and meso-level dynamics. In 2025, he succeeded Tom Snijders as maintainer of the RSiena package.
Content
List of Illustrations; List of Tables; Preface; Part I. Network Dynamics: 1. Network dynamics: ties have consequences; 2. Longitudinal network data; 3. Actor-oriented models: fundamentals; 4. What drives network dynamics: effects; 5. Parameter estimation and testing; 6. Actor-oriented models: elaboration; 7. Rate functions; 8. Creating new and maintaining existing ties; 9. Local models, global consequences; 10. Absent data; Part II. Networks and Behavior: 11. Behavior change in a network context; 12. Longitudinal behavior data; 13. Modeling network-behavior co-evolution; 14. Effects for behavior; 15. Estimation and model construction for co-evolution models; 16. Diffusion of innovations; 17. Goodness-of-fit and other uses of simulated data; Part III. Special Data Structures: 18. Non-directed networks; 19. Multivariate networks; 20. Valued networks; 21. Two-mode networks; 22. Multiple groups; Glossary; References; Index.