  Variational methods for parameter estimation are an active research area,
potentially offering computationally tractable heuristics with theoretical
performance bounds. We build on recent work that applies such methods to
network data, and establish asymptotic normality rates for parameter estimates
of stochastic blockmodel data, by either maximum likelihood or variational
estimation. The result also applies to various sub-models of the stochastic
blockmodel found in the literature.
