Replication Data for: Detecting Heterogeneity and Inferring Latent Roles in Longitudinal Networks
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'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' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Network analysis","Node clustering","Role analysis"]} <|eot_id|>
 Here is the Description of a dataset. Description:'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' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
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
Network analysis; Node clustering; Role analysis