  We present a method to find the best temporal partition at any time-scale and
rank the relevance of partitions found at different time-scales. This method is
based on random walkers coevolving with the network and as such constitutes a
generalization of partition stability to the case of temporal networks. We show
that, when applied to a toy model and real datasets, temporal stability
uncovers structures that are persistent over meaningful time-scales as well as
important isolated events, making it an effective tool to study both abrupt
changes and gradual evolution of a network mesoscopic structures.
