  In this paper we explore the PageRank of temporal networks on both discrete
and continuous time scales in the presence of personalization vectors that vary
over time. Also the underlying interplay between the discrete and continuous
settings arising from discretization is highlighted. Additionally, localization
results that set bounds to the estimated influence of the personalization
vector on the ranking of a particular node are given. The theoretical results
are illustrated by means of some real and synthetic examples.
