  Although many tools have been developed and employed to characterize temporal
networks, the issue of how to compare them remains largely open. It depends
indeed on what features are considered as relevant, and on the way the
differences in these features are quantified. In this paper, we propose to
characterize temporal networks through their behaviour under general
transformations that are local in time: (i) a local time shuffling, which
destroys correlations at time scales smaller than a given scale $b$, while
preserving large time scales, and (ii) a local temporal aggregation on time
windows of length $n$. By varying $b$ and $n$, we obtain a flow of temporal
networks, and flows of observable values, which encode the phenomenology of the
temporal network on multiple time scales. We use a symbolic approach to
summarize these flows into labels (strings of characters) describing their
trends. These labels can then be used to compare temporal networks, validate
models, or identify groups of networks with similar labels. Our procedure can
be applied to any temporal network and with an arbitrary set of observables,
and we illustrate it on an ensemble of data sets describing face-to-face
interactions in various contexts, including both empirical and synthetic data.
