Asymptotic normality of maximum likelihood and its variational   approximation for stochastic blockmodels
  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. 
Peter Bickel, David Choi, Xiangyu Chang, Hai Zhang
math.ST cs.SI stat.TH