  One of the most common approaches to the analysis of dynamic networks is
through time-window aggregation. The resulting representation is a sequence of
static networks, i.e. the snapshot graph. Despite this representation being
widely used in the literature, a general framework to evaluate the soundness of
snapshot graphs is still missing. In this article, we propose two scores to
quantify conflicting objectives: Stability measures how much stable the
sequence of snapshots is, while Fidelity measures the loss of information
compared to the original data. We also develop a technique of targeted
filtering of the links, to simplify the original temporal network. Our
framework is tested on datasets of proximity and face-to-face interactions.
