  We propose a novel model-selection method for dynamic networks. Our approach
involves training a classifier on a large body of synthetic network data. The
data is generated by simulating nine state-of-the-art random graph models for
dynamic networks, with parameter range chosen to ensure exponential growth of
the network size in time. We design a conceptually novel type of dynamic
features that count new links received by a group of vertices in a particular
time interval. The proposed features are easy to compute, analytically
tractable, and interpretable. Our approach achieves a near-perfect
classification of synthetic networks, exceeding the state-of-the-art by a large
margin. Applying our classification method to real-world citation networks
gives credibility to the claims in the literature that models with preferential
attachment, fitness and aging fit real-world citation networks best, although
sometimes, the predicted model does not involve vertex fitness.
