  Oversampling is a common characteristic of data representing dynamic
networks. It introduces noise into representations of dynamic networks, but
there has been little work so far to compensate for it. Oversampling can affect
the quality of many important algorithmic problems on dynamic networks,
including link prediction. Link prediction seeks to predict edges that will be
added to the network given previous snapshots. We show that not only does
oversampling affect the quality of link prediction, but that we can use link
prediction to recover from the effects of oversampling. We also introduce a
novel generative model of noise in dynamic networks that represents
oversampling. We demonstrate the results of our approach on both synthetic and
real-world data.
