# Hausdorff and Wasserstein metrics on graphs and other structured data

## TL;DR

This paper extends optimal transport metrics, including Wasserstein and Hausdorff, from set matching to structured data like graphs, enabling efficient, structure-preserving comparisons across diverse data types.

## Contribution

It introduces a framework for Wasserstein and Hausdorff metrics on structured data, generalizing optimal transport to graphs and other structures via category theory.

## Key findings

- Wasserstein metrics on structured data are convex relaxations of Hausdorff metrics.
- The Wasserstein metric on $C$-sets is computable via linear programming.
- The approach applies to various graph types and other structured data.

## Abstract

Optimal transport is widely used in pure and applied mathematics to find probabilistic solutions to hard combinatorial matching problems. We extend the Wasserstein metric and other elements of optimal transport from the matching of sets to the matching of graphs and other structured data. This structure-preserving form of optimal transport relaxes the usual notion of homomorphism between structures. It applies to graphs, directed and undirected, labeled and unlabeled, and to any other structure that can be realized as a $C$-set for some finitely presented category $C$. We construct both Hausdorff-style and Wasserstein-style metrics on $C$-sets and we show that the latter are convex relaxations of the former. Like the classical Wasserstein metric, the Wasserstein metric on $C$-sets is the value of a linear program and is therefore efficiently computable.