A Theoretical and Empirical Comparison of the Temporal Exponential Random Graph Model and the Stochastic Actor-Oriented Model

TL;DR

This paper compares the theoretical and empirical performance of the Temporal Exponential Random Graph Model (TERGM) and the Stochastic Actor-Oriented Model (SAOM) in longitudinal network analysis, highlighting their similarities and differences.

Contribution

It provides a comprehensive comparison of TERGM and SAOM through theory, simulation, and real data, clarifying their relative strengths and weaknesses.

Findings

Models behave similarly under certain specifications.

Each model outperforms the other when its assumptions are met.

Theoretical differences influence model performance.

Abstract

The temporal exponential random graph model (TERGM) and the stochastic actor-oriented model (SAOM, e.g., SIENA) are popular models for longitudinal network analysis. We compare these models theoretically, via simulation, and through a real-data example in order to assess their relative strengths and weaknesses. Though we do not aim to make a general claim about either being superior to the other across all specifications, we highlight several theoretical differences the analyst might consider and find that with some specifications, the two models behave very similarly, while each model out-predicts the other one the more the specific assumptions of the respective model are met.