Graph Learning Over Polytopic Uncertain Graph

Abstract

This letter introduces a graph learning approach leveraging prior knowledge of graph topology. For this, we integrate the concept of polytopic uncertainty into existing approaches that learn graph Laplacians and adjacency matrices, constraining the solution space to a polytopic set. Our approach offers improved accuracy with reduced computational cost by focusing on a smaller solution space, effectively excluding implausible topologies. Numerical experiments demonstrate superior learned graph quality compared to existing approaches across various signal models and noise levels.