  In temporal networks, where nodes interact via sequences of temporary events,
information or resources can only flow through paths that follow the
time-ordering of events. Such temporal paths play a crucial role in dynamic
processes. However, since networks have so far been usually considered static
or quasi-static, the properties of temporal paths are not yet well understood.
Building on a definition and algorithmic implementation of the average temporal
distance between nodes, we study temporal paths in empirical networks of human
communication and air transport. Although temporal distances correlate with
static graph distances, there is a large spread, and nodes that appear close
from the static network view may be connected via slow paths or not at all.
Differences between static and temporal properties are further highlighted in
studies of the temporal closeness centrality. In addition, correlations and
heterogeneities in the underlying event sequences affect temporal path lengths,
increasing temporal distances in communication networks and decreasing them in
the air transport network.
