Stochastic Actor-Oriented Models for Longitudinal Networks

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.