  In this work, we describe a method that determines an exact map from a finite
set of subgraph densities to the parameters of a stochastic block model (SBM)
matching these densities. Given a number $K$ of blocks, the subgraph densities
of a finite number of stars and bistars uniquely determines a single element of
the class of all degree-separated stochastic block models with $K$ blocks. Our
method makes it possible to translate estimates of these subgraph densities
into model parameters, and hence to use subgraph densities directly for
inference. The computational overhead is negligible; computing the translation
map is polynomial in $K$, but independent of the graph size once the subgraph
densities are given.
