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.