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arXiv · 2204.14234

Vietoris$\unicode{x2013}$Rips Complexes of Metric Spaces Near a Metric Graph

Abstract

For a sufficiently small scale $\beta>0$, the Vietoris$\unicode{x2013}$Rips complex $\mathcal{R}_\beta(S)$ of a metric space $S$ with a small Gromov$\unicode{x2013}$Hausdorff distance to a closed Riemannian manifold $M$ has been already known to recover $M$ up to homotopy type. While the qualitative result is remarkable and generalizes naturally to the recovery of spaces beyond Riemannian manifolds$\unicode{x2014}$such as geodesic metric spaces with a positive convexity radius$\unicode{x2014}$the generality comes at a cost. Although the scale parameter $\beta$ is known to depend only on the geometric properties of the geodesic space, how to quantitatively choose such a $\beta$ for a given geodesic space is still elusive. In this work, we focus on the topological recovery of a special type of geodesic space, called a metric graph. For an abstract metric graph $\mathcal{G}$ and a (sample) metric space $S$ with a small Gromov$\unicode{x2013}$Hausdorff distance to it, we provide a description of $\beta$ based on the convexity radius of $\mathcal{G}$ in order for $\mathcal{R}_\beta(S)$ to be homotopy equivalent to $\mathcal{G}$. Our investigation also extends to the study of the Vietoris$\unicode{x2013}$Rips complexes of a Euclidean subset $S\subset\mathbb{R}^d$ with a small Hausdorff distance to an embedded metric graph $\mathcal{G}\subset\mathbb{R}^d$. From the pairwise Euclidean distances of points of $S$, we introduce a family (parametrized by $\varepsilon$) of path-based Vietoris$\unicode{x2013}$Rips complexes $\mathcal{R}^\varepsilon_\beta(S)$ for a scale $\beta>0$. Based on the convexity radius and distortion of the embedding of $\mathcal{G}$, we show how to choose a suitable parameter $\varepsilon$ and a scale $\beta$ such that $\mathcal{R}^\varepsilon_\beta(S)$ is homotopy equivalent to $\mathcal{G}$.

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BibTeXRIS

Sushovan Majhi. 2022-04-29. Vietoris$\unicode{x2013}$Rips Complexes of Metric Spaces Near a Metric Graph. https://doi.org/10.13140/rg.2.2.20986.36804

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