English

Mixing and Merging Metric Spaces using Directed Graphs

Combinatorics 2025-07-10 v2 Information Theory math.IT Metric Geometry Statistics Theory Statistics Theory

Abstract

Let (X1,d1),,(XN,dN)(X_1,d_1),\dots, (X_N,d_N) be metric spaces, where di:Xi×Xi[0,1]d_i: X_i \times X_i \rightarrow [0,1] is a distance function for i=1,,Ni=1,\dots,N. Let X\mathcal{X} denote the set theoretic product X1××XNX_1\times \cdots \times X_N. Let G=(V,E)\mathcal{G} = \left(\mathcal{V},\mathcal{E}\right) be a directed graph with vertex set V={1,,N}\mathcal{V} =\{1,\dots, N\}, and let P={pij}\mathcal{P} = \{p_{ij}\} be a collection of weights, where each pij(0,1]p_{ij}\in (0, 1] is associated with the edge (i,j)E(i,j) \in \mathcal{E}. We introduce the function dX,G,P:X×X[0,1]d_{\mathcal{X},\mathcal{G},\mathcal{P}}: \mathcal{X}\times \mathcal{X} \to [0,1] defined by \begin{align*} d_{\mathcal{X},\mathcal{G},\mathcal{P}}(\mathbf{g},\mathbf{h}) := \left(1 - \frac{1}{N}\sum_{j=1}^N \prod_{i=1}^N \left[1- d_i(g_i,h_i)\right]^{\frac{1}{p_{ji}}} \right), \end{align*} for all g,hX\mathbf{g},\mathbf{h} \in \mathcal{X}. In this paper we show that dX,G,Pd_{\mathcal{X},\mathcal{G},\mathcal{P}} defines a metric space over X\mathcal{X}. Then we determine how this distance behaves under various graph operations, including disjoint unions and Cartesian products. We investigate two limiting cases: (a) when dX,G,Pd_{\mathcal{X},\mathcal{G},\mathcal{P}} is defined over a finite field, leading to a broad generalization of graph-based distances commonly studied in error-correcting code theory; and (b) when the metric is extended to graphons, enabling the measurement of distances in a continuous graph limit setting.

Keywords

Cite

@article{arxiv.2505.06405,
  title  = {Mixing and Merging Metric Spaces using Directed Graphs},
  author = {Mahir Bilen Can and Shantanu Chakrabartty},
  journal= {arXiv preprint arXiv:2505.06405},
  year   = {2025}
}
R2 v1 2026-06-28T23:27:47.952Z