Introducing Graph Learning over Polytopic Uncertain Graph

arXiv:2404.08176v1 Announce Type: cross
Abstract: 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.

abstract arxiv cases class cs.lg eess.sp graph graph learning lies parameters set the graph type uncertain uncertainty