Gromov-Wasserstein Bound between Reeb and Mapper Graphs

TL;DR

This paper introduces a Gromov-Wasserstein metric to compare Reeb and Mapper graphs as metric measure spaces, enabling better incorporation of data distribution and providing convergence rates with practical numerical validation.

Contribution

It proposes a novel Gromov-Wasserstein based approach to compare Reeb and Mapper graphs considering measure information, with theoretical convergence guarantees.

Findings

Derived convergence rates for Mapper to Reeb graph comparison.

Demonstrated the effectiveness of the metric through numerical experiments.

Abstract

Since its introduction as a computable approximation of the Reeb graph, the Mapper graph has become one of the most popular tools from topological data analysis for performing data visualization and inference. However, finding an appropriate metric (that is, a tractable metric with theoretical guarantees) for comparing Reeb and Mapper graphs, in order to, e.g., quantify the rate of convergence of the Mapper graph to the Reeb graph, is a difficult problem. While several metrics have been proposed in the literature, none is able to incorporate measure information, when data points are sampled according to an underlying probability measure. The resulting Reeb and Mapper graphs are therefore purely deterministic and combinatorial, and substantial effort is thus required to ensure their statistical validity. In this article, we handle this issue by treating Reeb and Mapper graphs as metric measure spaces. This allows us to use Gromov-Wasserstein metrics to compare these graphs directly in order to better incorporate the probability measures that data points are sampled from. Then, we describe the geometry that arises from this perspective, and we derive rates of convergence of the Mapper graph to the Reeb graph in this context. Finally, we showcase the usefulness of such metrics for Reeb and Mapper graphs in a few numerical experiments.