  We study the minimum cut problem in the presence of uncertainty and show how
to apply a novel robust optimization approach, which aims to exploit the
similarity in subsequent graph measurements or similar graph instances, without
posing any assumptions on the way they have been obtained. With experiments we
show that the approach works well when compared to other approaches that are
also oblivious towards the relationship between the input datasets.
