Abstract
Social media networks are dynamic. As such, the order in which network ties develop is an important aspect of the network dynamics.This study proposes a novel dynamic network model, the Nodal Attribute-based Temporal Exponential Random Graph Model (NATERGM) for dynamic network analysis. The proposed model focuses on how the nodal attributes of a network affect the order in which the network ties develop. Empirical results showed that the NATERGM demonstrated an enhanced pattern testing capability compared to benchmark models. The proposed NATERGM model helps explain the roles of nodal attributes in the formation process of dynamic networks.
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