  Statistical analysis of network is an active research area and the literature
counts a lot of papers concerned with network models and statistical analysis
of networks. However, very few papers deal with missing data in network
analysis and we reckon that, in practice, networks are often observed with
missing values. In this paper we focus on the Stochastic Block Model with
valued edges and consider a MCAR setting by assuming that every dyad (pair of
nodes) is sampled identically and independently of the others with probability
$\rho > 0$. We prove that maximum likelihood estimators and its variational
approximations are consistent and asymptotically normal in the presence of
missing data as soon as the sampling probability $\rho$ satisfies
$\rho\gg\log(n)/n$.
