Modeling Tie Duration in ERGM-Based Dynamic Network Models

TL;DR

This paper explores modeling tie durations in Separable Temporal ERGMs, proposing methods to incorporate various duration distributions under Markov assumptions for dynamic social network analysis.

Contribution

It introduces new approaches to model a variety of tie duration distributions within the STERGM framework under Markov assumptions.

Findings

Demonstrates how to model different duration distributions in STERGMs.

Provides methods for incorporating hazard structures with Markov assumptions.

Enhances the flexibility of dynamic network modeling.

Abstract

Krivitsky and Handcock (2014) proposed a Separable Temporal ERGM (STERGM) framework for modeling social networks, which facilitates separable modeling of the tie duration distributions and the structural dynamics of tie formation. In this note, we explore the hazard structures achievable in this framework, with first- and higher-order Markov assumptions, and propose ways to model a variety of duration distributions in this framework.