  Temporal networks have been increasingly used to model a diversity of systems
that evolve in time; for example human contact structures over which dynamic
processes such as epidemics take place. A fundamental aspect of real-life
networks is that they are sampled within temporal and spatial frames.
Furthermore, one might wish to subsample networks to reduce their size for
better visualization or to perform computationally intensive simulations. The
sampling method may affect the network structure and thus caution is necessary
to generalize results based on samples. In this paper, we study four sampling
strategies applied to a variety of real-life temporal networks. We quantify the
biases generated by each sampling strategy on a number of relevant statistics
such as link activity, temporal paths and epidemic spread. We find that some
biases are common in a variety of networks and statistics, but one strategy,
uniform sampling of nodes, shows improved performance in most scenarios. Our
results help researchers to better design network data collection protocols and
to understand the limitations of sampled temporal network data.
