  Navigation on graphs is the problem how an agent walking on the graph can get
from a source to a target with limited information about the graph. The
information and the way to exploit it can vary. In this paper, we study
navigation on temporal networks -- networks where we have explicit information
about the time of the interaction, not only who interacts with whom. We
contrast a type of greedy navigation -- where agents follow paths that would
have worked well in the past -- with two strategies that do not exploit the
additional information. We test these on empirical temporal network data sets.
The greedy navigation is indeed more efficient than the reference strategies,
meaning that there are correlations in the real temporal networks that can be
exploited. We find that the navigability for individual nodes is most strongly
correlated with degree and burstiness, i.e., both topological and temporal
structures affect the navigation efficiency.
