Stochastic Actor-Oriented Models for Longitudinal Networks

This is a book about the statistical analysis of longitudinal social network data for researchers and students in the social and other sciences. It treats the interdependent dynamics of networks with other networks and social influence on behavior and attitudes. It is linked to open-source software and gives practical guidance.

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.