  Detecting the time evolution of the community structure of networks is
crucial to identify major changes in the internal organization of many complex
systems, which may undergo important endogenous or exogenous events. This
analysis can be done in two ways: considering each snapshot as an independent
community detection problem or taking into account the whole evolution of the
network. In the first case, one can apply static methods on the temporal
snapshots, which correspond to configurations of the system in short time
windows, and match afterwards the communities across layers. Alternatively, one
can develop dedicated dynamic procedures, so that multiple snapshots are
simultaneously taken into account while detecting communities, which allows us
to keep memory of the flow. To check how well a method of any kind could
capture the evolution of communities, suitable benchmarks are needed. Here we
propose a model for generating simple dynamic benchmark graphs, based on
stochastic block models. In them, the time evolution consists of a periodic
oscillation of the system's structure between configurations with built-in
community structure. We also propose the extension of quality comparison
indices to the dynamic scenario.
