Scott Duxbury’s
Chapter 1 sets the stage by overviewing what longitudinal network models are and why we need them. There is useful nuts and bolts guidance regarding data structures and ways of organizing network panel data (i.e., snapshots of a full network at multiple time points). A section on modeling capabilities offers entree to the multitude of research questions network models can address. This section also underscores why network analysis can be daunting—some researchers are interested in explaining change in a network; others are interested in network effects on behavior change; and still others are interested in both. Each of these calls for a different modeling approach. The chapter concludes by discussing how model choice should be driven by theoretical concerns regarding the nature of ties and their change.
The meat of the book is Chapters 2-5, which review a total of six models. Chapters 2 and 3 each cover one of the more widely used approaches to modeling network panel data. The Temporal Exponential Random Graph Model (TERGM) and Stochastic Actor-Oriented Model (SAOM) can answer similar questions about how networks change. Duxbury provides guidance to model selection by way of differences in their assumptions and how they model change, with implications for their interpretation and suitability for various types of relations. Whereas these models assume ties are persisting “states” (e.g., friendships), Chapter 4 focuses on networks of relational events, where ties are fleeting and dependence accumulates across time. The focus here is on modeling which relational event happens
Chapter 5 “flips” the research question by discussing methods for testing network influence, whereby actor covariates are the outcome of interest and network measures are predictors. Two models are described: the Temporal Network Autocorrelation Model (TNAM) and a variation of the SAOM from Chapter 3. A key distinction is that the TNAM treats the network as exogenous, while an SAOM models endogenous change in both individual covariates and networks (i.e., co-evolution). Duxbury discusses the assumptions and trade-offs of each approach and their implications for the types of questions the models can answer.
Chapter 6 concludes by reviewing some current issues in network modeling (e.g., missing data, measurement error, unobserved heterogeneity). Duxbury summarizes current understandings and best practices and points to where greater methodological development is needed.
The book excels at helping the reader wrap their head around these modeling approaches, facilitated by a general overview of each model’s assumptions, specification, and advanced considerations. There is also a helpful “intuition” section that describes each model using a simplified framework likely to be more familiar to the general reader (e.g., think of a TERGM as a logistic regression). I appreciated the start-to-finish examples for many of the models, which covered data format, model setup and estimation, goodness of fit, and interpretation. Duxbury deserves credit for showing how to use average marginal effects and predicted plots to assist with interpretation.
Perhaps the greatest value in this book is in helping to point the reader in the right direction. The book excels at conveying the capabilities of each modeling framework and providing direction on which model to choose based on one’s data and research question. There is enough detail to give the reader a rough outline of the data requirements and analysis steps.
Covering these sophisticated models in so few pages is a challenging task. Accompanying R scripts for the examples help fill in some of the blanks. Yet, lingering questions are unavoidable. I found the coverage of the REM model to be the most wanting, mainly due to the counterintuitive nature of the example data. Specifically, the REM was developed to take advantage of the exact timing or sequence of relational events. The example of online transactions between buyers and sellers is presented as such a case yet, without explanation, detailed information on event timing was discarded as transactions were aggregated into monthly snapshots (resulting in data that resembles the panel data used by a TERGM or SAOM). This made it difficult to align the example with the model description. I was also left wondering about the motivation for using an REM versus a TERGM with such data and trade-offs between these approaches. More conventional relational event data could have avoided this confusion. The example also implies that high rates of simultaneous events are tolerable, but without insight into how the model accommodates this.
Given this book has the potential to be an important resource on many researchers’ pathways into network modeling, I am obligated to note several issues with interpretation in hopes of curtailing their diffusion. I highlight three in particular, each of which is a common pitfall when interpreting these models.
First, Duxbury makes the important point that when modeling higher-order structures, models should test for lower-order structures nested within them (e.g., two-stars nested within triads, sender/receiver effects nested within homophily terms). In turn, lower-order effects cannot be interpreted in isolation from significant higher-order terms, which is something that could have been more consistently reinforced to the reader. For instance, in Chapters 2 and 3, the interpretation of sender/receiver effects for program year are misleading because of the significant effect for program year homophily.
A second issue relates to how the standard implementations of TERGMs and SAOMs do not differentiate tie formation from tie dissolution. Duxbury notes this, but then the language used in describing effects occasionally slips into a “tie formation” explanation. Understandably this allows for simpler prose, but it risks mischaracterizing the model.
The third issue pertains to interpreting effects in the behavior function of a SAOM. While this function does take the form of a multinomial logistic regression predicting the adoption of different levels of a behavior, it does not acknowledge an actor’s current behavior state. Hence, the model is agnostic about whether adopting a particular behavior level represents an increase, decrease, or stability.
In sum, this book is a useful resource for researchers considering a dynamic network analysis and who want direction on which modeling approach to take, types of questions that can be answered, and what goes into an analysis.