  Nodes can be ranked according to their relative importance within the
network. Ranking algorithms based on random walks are particularly useful
because they connect topological and diffusive properties of the network.
Previous methods based on random walks, as for example the PageRank, have
focused on static structures. However, several realistic networks are indeed
dynamic, meaning that their structure changes in time. In this paper, we
propose a centrality measure for temporal networks based on random walks which
we call TempoRank. While in a static network, the stationary density of the
random walk is proportional to the degree or the strength of a node, we find
that in temporal networks, the stationary density is proportional to the
in-strength of the so-called effective network. The stationary density also
depends on the sojourn probability q which regulates the tendency of the walker
to stay in the node. We apply our method to human interaction networks and show
that although it is important for a node to be connected to another node with
many random walkers at the right moment (one of the principles of the
PageRank), this effect is negligible in practice when the time order of link
activation is included.
