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

Improving ERGM Starting Values Using Simulated Annealing