  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.
