Longitudinal Network Models

Scott Duxbury is an Assistant Professor of Sociology at the University of North Carolina at Chapel Hill. His research examines drug markets, criminal networks, quantitative and computational methods, public opinion, punishment, racism, and the criminal justice system. It has appeared in American Sociological Review, American Journal of Sociology, and Social Forces, among other outlets. Scott's book, Longitudinal Network Models, provides an introductory text to the suite of statistical models available for longitudinal network data analysis.

Chapter 1. Introduction Chapter 2: Temporal Exponential Random Graph Models Chapter 3: Stochastic Actor-oriented Models Chapter 4: Modeling Relational Event Data Chapter 5: Network Influence Models Chapter 6: Conclusion

A brilliant 'how to' for modelling dynamic network data. An exquisite balance of model intuition, assumptions and practical advice, accessible to all network / data scientists. -- Alexander John Bond This is a very timely book that provides critical skills for conducting explanatory analysis of longitudinal social network data. Both beginners, and advanced analysts can benefit from reading this book as it provides many real life examples, illustrating computational processes, interpreting results, and even furnishing R codes. For those who aspire to learn advanced topics in analyzing longitudinal social network data, this is a must-have book. -- Song Yang This book presents the state-of-art of longitudinal network analysis. It is comprehensive while staying concise, well structured, and clearly written. Definitely a moneyball in the field! -- Weihua An