Missing data are often problematic when analyzing complete longitudinal social network data. We review approaches for accommodating missing data when analyzing longitudinal network data with stochastic actor-based models. One common practice is to restrict analyses to participants observed at most or all time points, to achieve model convergence. We propose and evaluate an alternative, more inclusive approach to sub-setting and analyzing longitudinal network data, using data from a school friendship network observed at four waves (N =694). Compared to standard practices, our approach retained more information from partially observed participants, generated a more representative analytic sample, and led to less biased model estimates for this case study. The implications and potential applications for longitudinal network analysis are discussed.

We develop a class of exponential-family point processes based on a latent social space to model the coevolution of social structure and behavior over time. Temporal dynamics are modeled as a discrete Markov process specified through individual transition distributions for each actor in the system at a given time. We prove that these distributions have an analytic closed form under certain conditions and use the result to develop likelihood-based inference. We provide a computational framework to enable both simulation and inference in practice. Finally, we demonstrate the value of these models by analyzing alcohol and drug use over time in the context of adolescent friendship networks.

Collaboration networks are thought to be desirable to foster both individual and population productivity. Often programs are implemented to promote collaboration, for example, at academic institutions. However, few tools are available to assess the efficacy of these programs, and very few are datadriven. We carried out a survey at California State University, San Marcos during the 2012-2013 academic year to measure five types of collaboration ties among professors in five science departments at the university over time.During the time period of study, professors participated in NIH-sponsored curriculum development activities with members of other departments. It was hypothesized that participation in these activities would also foster overall collaboration between these departments.This survey enables the exploration of several methodological and theoretical challenges in network research. In this paper we develop a statistical approach to assess the impact of programmatic interventions on collaboration using model-assisted social network analysis. We derive and implement a hierarchical Bayesian approach to modeling error-prone responses in surveys and examine the effect of an intervention on network structure. Based on this analysis we find an increase in educational collaboration over time after adjusting for the length of time each professor had to form collaborative ties at the university.

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