  Given a set of snapshots from a temporal network we develop, analyze, and
experimentally validate a so-called network interpolation scheme. Our method
allows us to build a plausible, albeit random, sequence of graphs that
transition between any two given graphs. Importantly, our model is well
characterized by a Markov chain, and we leverage this representation to
analytically estimate the hitting time (to a predefined distance to the target
graph) and long term behavior of our model. These observations also serve to
provide interpretation and justification for a rate parameter in our model.
Lastly, through a mix of synthetic and real-world data experiments we
demonstrate that our model builds reasonable graph trajectories between
snapshots, as measured through various graph statistics. In these experiments,
we find that our interpolation scheme compares favorably to common network
growth models, such as preferential attachment and triadic closure.
