A Separable Model for Dynamic Networks

TL;DR

This paper introduces a novel Separable Temporal ERGM (STERGM) model for dynamic networks, enabling flexible and interpretable analysis of evolving social ties over time.

Contribution

It presents a new discrete-time generative model for dynamic networks that separates tie duration from structural formation, with methods for likelihood inference.

Findings

Effective modeling of social network evolution

Likelihood-based inference algorithms developed

Application to school friendship network data

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.