P.Barucca, P.Mazzarisi, F.Lillo, D.Tantari (2017), Disentangling group and link persistence in dynamic stochastic block models

We study the inference of a model of dynamic networks in which both communities and
links keep memory of previous network states. By considering maximum likelihood inference from
single snapshot observations of the network, we show that link persistence makes the inference of
communities harder, decreasing the detectability threshold, while community persistence tends to make
it easier. We analytically show that communities inferred from single network snapshot can share a
maximum overlap with the underlying communities of a specific previous instant in time. This leads
to time-lagged inference: the identification of past communities rather than present ones. Finally
we compute the time lag and propose a corrected algorithm, the Lagged Snapshot Dynamic (LSD)
algorithm, for community detection in dynamic networks. We analytically and numerically characterize
the detectability transitions of such algorithm as a function of the memory parameters of the model.