Replication Data for: Detecting Heterogeneity and Inferring Latent Roles in Longitudinal Networks

Network analysis has typically examined the formation of whole networks while neglecting variation within or across networks. Actors within networks often adopt particular roles. While cross-sectional approaches for inferring latent roles exist, there is a paucity of approaches for considering roles in longitudinal networks. This paper explores the conceptual dynamics of temporally observed roles while deriving and introducing a novel statistical tool, the ego-TERGM, capable of uncovering these latent dynamics. Estimated through an Expectation-Maximization algorithm, the ego-TERGM is quick and accurate in classifying roles within a broader temporal network. An application to the Kapferer strike network illustrates the model's utility.