Introducing Graph Learning over Polytopic Uncertain Graph

TL;DR

This paper presents a novel graph learning method that accounts for polytopic uncertainty in the graph structure, improving performance and efficiency in uncertain graph scenarios.

Contribution

It introduces a new approach integrating polytopic uncertainty into existing graph learning frameworks, enhancing robustness and reducing computational costs.

Findings

Improved accuracy in uncertain graph learning tasks

Reduced computational complexity

Enhanced robustness to graph variations

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.