  We study opinion dynamics over a directed multilayer network. In particular,
we consider networks in which the impact of neighbors of agents on their
opinions is proportional to their in-degree. Agents update their opinions over
time to coordinate with their neighbors. However, the frequency of agents'
interactions with neighbors in different network layers differs. Consequently,
the multilayer network's adjacency matrices are time-varying. We aim to
characterize how the frequency of activation of different layers impacts the
convergence of the opinion dynamics process.
