  Random walks are basic diffusion processes on networks and have applications
in, for example, searching, navigation, ranking, and community detection.
Recent recognition of the importance of temporal aspects on networks spurred
studies of random walks on temporal networks. Here we theoretically study two
types of event-driven random walks on a stochastic temporal network model that
produces arbitrary distributions of interevent-times. In the so-called active
random walk, the interevent-time is reinitialized on all links upon each
movement of the walker. In the so-called passive random walk, the
interevent-time is only reinitialized on the link that has been used last time,
and it is a type of correlated random walk. We find that the steady state is
always the uniform density for the passive random walk. In contrast, for the
active random walk, it increases or decreases with the node's degree depending
on the distribution of interevent-times. The mean recurrence time of a node is
inversely proportional to the degree for both active and passive random walks.
Furthermore, the mean recurrence time does or does not depend on the
distribution of interevent-times for the active and passive random walks,
respectively.
