Stochastic Actor-Oriented Models for Longitudinal Networks

AvTom A.B. Snijders,Christian Steglich

Häftad, Engelska, 2026

Del 55 i serien Structural Analysis in the Social Sciences

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2026-10-31
Format:Häftad
Språk:Engelska
Serie:Structural Analysis in the Social Sciences
Antal sidor:500
Förlag:Cambridge University Press
ISBN:9781009843683

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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, Linköping 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.

Innehållsförteckning

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.