Improving ERGM Starting Values Using Simulated Annealing

TL;DR

This paper introduces a simulated annealing approach to improve starting values for ERGM MLE estimation, successfully estimating a challenging network model where other methods failed.

Contribution

It proposes a novel simulated annealing method to find better starting points for ERGM MLE estimation, enhancing estimation success for difficult models.

Findings

Successfully estimated a challenging ERGM using the proposed method.

Improved starting values led to convergence where other methods failed.

Demonstrated the method's effectiveness on real network data.

Abstract

Much of the theory of estimation for exponential family models, which include exponential-family random graph models (ERGMs) as a special case, is well-established and maximum likelihood estimates in particular enjoy many desirable properties. However, in the case of many ERGMs, direct calculation of MLEs is impossible and therefore methods for approximating MLEs and/or alternative estimation methods must be employed. Many MLE approximation methods require alternative estimates as starting points. We discuss one class of such alternatives here. The MLE satisfies the so-called "likelihood principle," unlike the MPLE. This means that different networks may have different MPLEs even if they have the same sufficient statistics. We exploit this fact here to search for improved starting values for approximation-based MLE methods. The method we propose has shown its merit in producing an MLE for a network dataset and model that had defied estimation using all other known methods.