Metric Decompositions of Path-Separable Graphs

TL;DR

This paper introduces a new metric decomposition technique for p-path-separable graphs, improving bounds over general graphs and applying to specific families like bounded-treewidth graphs.

Contribution

It designs low-diameter decompositions for p-path-separable graphs, refining previous bounds and extending applicability to new graph families.

Findings

Achieves O(log(p log n)) decomposition for p-path-separable graphs.

Refines bounds from O(log n) for general graphs.

Provides new bounds for bounded-treewidth graphs.

Abstract

A prominent tool in many problems involving metric spaces is a notion of randomized low-diameter decomposition. Loosely speaking, $\beta$-decomposition refers to a probability distribution over partitions of the metric into sets of low diameter, such that nearby points (parameterized by $\beta>0$) are likely to be "clustered" together. Applying this notion to the shortest-path metric in edge-weighted graphs, it is known that $n$-vertex graphs admit an $O(\ln n)$-padded decomposition (Bartal, 1996), and that excluded-minor graphs admit $O(1)$-padded decomposition (Klein, Plotkin and Rao 1993, Fakcharoenphol and Talwar 2003, Abraham et al. 2014). We design decompositions to the family of $p$-path-separable graphs, which was defined by Abraham and Gavoille (2006). and refers to graphs that admit vertex-separators consisting of at most $p$ shortest paths in the graph. Our main result is that every $p$-path-separable $n$-vertex graph admits an $O(\ln (p \ln n))$-decomposition, which refines the $O(\ln n)$ bound for general graphs, and provides new bounds for families like bounded-treewidth graphs. Technically, our clustering process differs from previous ones by working in (the shortest-path metric of) carefully chosen subgraphs.