  Chatterjee, Diaconis and Sly (2011) recently established the consistency of
the maximum likelihood estimate in the $\beta$-model when the number of
vertices goes to infinity. By approximating the inverse of the Fisher
information matrix, we obtain its asymptotic normality under mild conditions.
Simulation studies and a data example illustrate the theoretical results.
