  Analysis of stochastic models of networks is quite important in light of the
huge influx of network data in social, information and bio sciences, but a
proper statistical analysis of features of different stochastic models of
networks is still underway. We propose bootstrap subsampling methods for
finding empirical distribution of count features or ``moments'' (Bickel, Chen
and Levina [Ann. Statist. 39 (2011) 2280-2301]) and smooth functions of these
features for the networks. Using these methods, we cannot only estimate the
variance of count features but also get good estimates of such feature counts,
which are usually expensive to compute numerically in large networks. In our
paper, we prove theoretical properties of the bootstrap estimates of variance
of the count features as well as show their efficacy through simulation. We
also use the method on some real network data for estimation of variance and
expectation of some count features.
