  This extended abstract introduces a class of graph learning applicable to
cases where the underlying graph has polytopic uncertainty, i.e., the graph is
not exactly known, but its parameters or properties vary within a known range.
By incorporating this assumption that the graph lies in a polytopic set into
two established graph learning frameworks, we find that our approach yields
better results with less computation.
