Introducing Graph Learning over Polytopic Uncertain Graph
This addresses graph learning for uncertain graphs, but it is incremental as it builds on existing frameworks.
The paper tackles graph learning when the underlying graph has polytopic uncertainty, where parameters vary within a known range, and finds that incorporating this assumption into established frameworks yields better results with less computation.
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.