  The abundance of models of complex networks and the current insufficient
validation standards make it difficult to judge which models are strongly
supported by data and which are not. We focus here on likelihood maximization
methods for models of growing networks with many parameters and compare their
performance on artificial and real datasets. While high dimensionality of the
parameter space harms the performance of direct likelihood maximization on
artificial data, this can be improved by introducing a suitable penalization
term. Likelihood maximization on real data shows that the presented approach is
able to discriminate among available network models. To make large-scale
datasets accessible to this kind of analysis, we propose a subset sampling
technique and show that it yields substantial model evidence in a fraction of
time necessary for the analysis of the complete data.
