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Longitudinal Network Models
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Longitudinal Network Models

  • Scott Duxbury - The University of North Carolina at Chapel Hill, USA


January 2023 | 160 pages | SAGE Publications, Inc

Although longitudinal social network data are increasingly collected, there are few guides on how to navigate the range of available tools for longitudinal network analysis. The applied social scientist is left to wonder: Which model is most appropriate for my data? How should I get started with this modeling strategy? And how do I know if my model is any good? This book answers these questions. Author Scott Duxbury assumes that the reader is familiar with network measurement, description, and notation, and is versed in regression analysis, but is likely unfamiliar with statistical network methods. The goal of the book is to guide readers towards choosing, applying, assessing, and interpreting a longitudinal network model, and each chapter is organized with a specific data structure or research question in mind. A companion website for the book at https://study.sagepub.com/researchmethods/qass/duxbury-longitudinal-network-models includes data and R code to replicate the examples in the book.

 
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

Supplements

Student Study Site
A website for the book at https://study.sagepub.com/researchmethods/qass/duxbury-longitudinal-network-models includes data and code to replicate the examples in the book.

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
Leeds Beckett University

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
University of Arkansas

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
Emory University

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