# Graph signal processing with categorical perspective

## TL;DR

This paper introduces a category theory-based framework for graph signal processing, aiming to unify and generalize existing probabilistic approaches that address uncertainties in signals and graphs.

## Contribution

It presents a novel categorical framework that broadens the theoretical foundation of graph signal processing and incorporates probabilistic considerations.

## Key findings

- Framework successfully unifies various probabilistic models
- Enhances understanding of signal and graph uncertainties
- Lays groundwork for future categorical methods in GSP

## Abstract

In this paper, we propose a framework for graph signal processing using category theory. The aim is to generalize a few recent works on probabilistic approaches to graph signal processing, which handle signal and graph uncertainties.