  Temporal networks are commonly used to represent dynamical complex systems
like social networks, simultaneous firing of neurons, human mobility or public
transportation. Their dynamics may evolve on multiple time scales
characterising for instance periodic activity patterns or structural changes.
The detection of these time scales can be challenging from the direct
observation of simple dynamical network properties like the activity of nodes
or the density of links. Here we propose two new methods, which rely on already
established static representations of temporal networks, namely supra-adjacency
matrices and temporal event graphs. We define dissimilarity metrics extracted
from these representations and compute their Fourier Transform to effectively
identify dominant periodic time scales characterising the original temporal
network. We demonstrate our methods using synthetic and real-world data sets
describing various kinds of temporal networks. We find that while in all cases
the two methods outperform the reference measures, the supra-adjacency based
method identifies more easily periodic changes in network density, while the
temporal event graph based method is better suited to detect periodic changes
in the group structure of the network. Our methodology may provide insights
into different phenomena occurring at multiple time-scales in systems
represented by temporal networks.
