# Introducing Graph Learning over Polytopic Uncertain Graph

arXiv: https://arxiv.org/abs/2404.08176 · HTML: https://mlsanity.com/paper/2404.08176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [How to learn a graph from smooth signals](https://mlsanity.com/paper/1601.02513/) (2016)
- [Polytopic uncertainty for linear systems: New and old complexity results](https://mlsanity.com/paper/1310.1930/) (2013)

## Abstract (arXiv)

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.