  We consider random walks on dynamical networks where edges appear and
disappear during finite time intervals. The process is grounded on three
independent stochastic processes determining the walker's waiting-time, the
up-time and down-time of edges activation. We first propose a comprehensive
analytical and numerical treatment on directed acyclic graphs. Once cycles are
allowed in the network, non-Markovian trajectories may emerge, remarkably even
if the walker and the evolution of the network edges are governed by memoryless
Poisson processes. We then introduce a general analytical framework to
characterize such non-Markovian walks and validate our findings with numerical
simulations.
