Application of the Dynamic Latent Space Model to Social Networks with Time-Varying Covariates

With the growing accessibility of tools such as online surveys and web scraping, longitudinal social network data are more commonly collected in social science research along with non-network survey data. Such data play a critical role in helping social scientists understand how relationships develop and evolve over time. Existing dynamic network models such as the Stochastic Actor-Oriented Model and the Temporal Exponential Random Graph Model provide frameworks to analyze traits of both the networks and the external non-network covariates. However, research on the dynamic latent space model (DLSM) has focused mainly on factors intrinsic to the networks themselves. Despite some discussion, the role of non-network data such as contextual or behavioral covariates remain a topic to be further explored in the context of DLSMs. In this study, one application of the DLSM to incorporate dynamic non-network covariates collected alongside friendship networks using autoregressive processes is presented. By analyzing two friendship network datasets with different time points and psychological covariates, it is shown how external factors can contribute to a deeper understanding of social interaction dynamics over time.