  Time-varying networks describe a wide array of systems whose constituents and
interactions evolve over time. They are defined by an ordered stream of
interactions between nodes, yet they are often represented in terms of a
sequence of static networks, each aggregating all edges and nodes present in a
time interval of size \Delta t. In this work we quantify the impact of an
arbitrary \Delta t on the description of a dynamical process taking place upon
a time-varying network. We focus on the elementary random walk, and put forth a
simple mathematical framework that well describes the behavior observed on real
datasets. The analytical description of the bias introduced by time integrating
techniques represents a step forward in the correct characterization of
dynamical processes on time-varying graphs.
