A Separable Model for Dynamic Networks

TLDR

Dynamic network models have manifold applications. The study develops a discrete‑time generative model for social network evolution that inherits the richness and flexibility of exponential‑family random graph models. The authors introduce a Separable Temporal ERGM that models tie duration and formation dynamics, and provide likelihood‑based inference with computational algorithms for maximum likelihood estimation. The model’s interpretability is demonstrated by analyzing a longitudinal friendship network in a school.

Abstract

Models of dynamic networks - networks that evolve over time - have manifold applications. We develop a discrete-time generative model for social network evolution that inherits the richness and flexibility of the class of exponential-family random graph models. The model - a Separable Temporal ERGM (STERGM) - facilitates separable modeling of the tie duration distributions and the structural dynamics of tie formation. We develop likelihood-based inference for the model, and provide computational algorithms for maximum likelihood estimation. We illustrate the interpretability of the model in analyzing a longitudinal network of friendship ties within a school.