  Random graph models are widely used to understand network properties and
graph algorithms. Key to such analyses are the different parameters of each
model, which affect various network features, such as its size, clustering, or
degree distribution. The exact effect of the parameters on these features is
not well understood, mainly because we lack tools to thoroughly investigate
this relation. Moreover, the parameters cannot be considered in isolation, as
changing one affects multiple features. Existing approaches for finding the
best model parameters of desired features, such as a grid search or estimating
the parameter-feature relations, are not well suited, as they are inaccurate or
computationally expensive.
  We introduce an efficient iterative fitting method, named ParFit, that finds
parameters using only a few network samples, based on the Robbins-Monro
algorithm. We test ParFit on three well-known graph models, namely
Erd\H{o}s-R\'enyi, Chung-Lu, and geometric inhomogeneous random graphs, as well
as on real-world networks, including web networks. We find that ParFit performs
well in terms of quality and running time across most parameter configurations.
