Vietoris--Rips Shadow for Euclidean Graph Reconstruction

TL;DR

This paper extends the understanding of Euclidean graph reconstruction by analyzing the homotopy and geometric properties of Vietoris--Rips complexes with path-based metrics, providing bounds for accurate topological and geometric recovery.

Contribution

It introduces a family of path-based Vietoris--Rips complexes and establishes quantitative bounds for their shadow projections to recover planar graphs in both homotopy and geometry.

Findings

Shadow projection induces -isomorphism under certain conditions

Provides bounds on sample density and scale for accurate reconstruction

Ensures homotopy-equivalent and Hausdorff-close embeddings of graphs

Abstract

The shadow of an abstract simplicial complex $K$ with vertices in $\mathbb{R}^N$ is a subset of $\mathbb{R}^N$ defined as the union of the convex hulls of simplices of $K$. The Vietoris--Rips complex of a metric space $(S,d)$ at scale $\beta$ is an abstract simplicial complex whose each $k$-simplex corresponds to $(k+1)$ points of $S$ within diameter $\beta$. In case $S\subset\mathbb R^2$ and $d(a,b)=\|a-b\|$ the standard Euclidean metric, the natural shadow projection of the Vietoris--Rips complex is already proved by Chambers et al. to induce isomorphisms on $\pi_0$ and $\pi_1$. We extend the result beyond the standard Euclidean distance on $S\subset\mathbb R^N$ to a family of path-based metrics, $d^\varepsilon_{S}$. From the pairwise Euclidean distances of points in $S$, we introduce a family (parametrized by $\varepsilon$) of path-based Vietoris--Rips complexes $R^\varepsilon_\beta(S)$ for a scale $\beta>0$. If $S\subset\mathbb{R}^2$ is Hausdorff-close to a planar Euclidean graph $G$, we provide quantitative bounds on scales $\beta,\varepsilon$ for the shadow projection map of the Vietoris--Rips complex of $(S,d^\varepsilon_S)$ at scale $\beta$ to induce $\pi_1$-isomorphism. This paper first studies the homotopy-type recovery of $G\subset\mathbb R^N$ using the abstract Vietoris--Rips complex of a Hausdorff-close sample $S$ under the $d^\varepsilon_S$ metric. Then, our result on the $\pi_1$-isomorphism induced by the shadow projection lends itself to providing also a geometrically close embedding for the reconstruction. Based on the length of the shortest loop and large-scale distortion of the embedding of $G$, we quantify the choice of a suitable sample density $\varepsilon$ and a scale $\beta$ at which the shadow of $R^\varepsilon_\beta(S)$ is homotopy-equivalent and Hausdorff-close to $G$.