Deterministic Low-Diameter Decompositions for Weighted Graphs and Distributed and Parallel Applications

TL;DR

This paper introduces new deterministic and distributed algorithms for low-diameter decompositions in weighted graphs, enabling efficient solutions to fundamental distance problems with polylogarithmic complexity.

Contribution

It extends low-diameter decomposition techniques to weighted graphs in a model-independent way, enabling new near-linear work parallel algorithms for low-stretch spanning trees and $ ext{l}_1$-embeddings.

Findings

First near-linear work, polylogarithmic depth deterministic algorithm for low-stretch spanning trees.

Deterministic algorithm for $ ext{l}_1$-embeddings with polylogarithmic distortion.

Improved round complexity for classical ball-carving in unweighted graphs.

Abstract

This paper presents new deterministic and distributed low-diameter decomposition algorithms for weighted graphs. In particular, we show that if one can efficiently compute approximate distances in a parallel or a distributed setting, one can also efficiently compute low-diameter decompositions. This consequently implies solutions to many fundamental distance based problems using a polylogarithmic number of approximate distance computations. Our low-diameter decomposition generalizes and extends the line of work starting from [Rozho\v{n}, Ghaffari STOC 2020] to weighted graphs in a very model-independent manner. Moreover, our clustering results have additional useful properties, including strong-diameter guarantees, separation properties, restricting cluster centers to specified terminals, and more. Applications include: -- The first near-linear work and polylogarithmic depth randomized and deterministic parallel algorithm for low-stretch spanning trees (LSST) with polylogarithmic stretch. Previously, the best parallel LSST algorithm required $m \cdot n^{o(1)}$ work and $n^{o(1)}$ depth and was inherently randomized. No deterministic LSST algorithm with truly sub-quadratic work and sub-linear depth was known. -- The first near-linear work and polylogarithmic depth deterministic algorithm for computing an $\ell_1$-embedding into polylogarithmic dimensional space with polylogarithmic distortion. The best prior deterministic algorithms for $\ell_1$-embeddings either require large polynomial work or are inherently sequential. Even when we apply our techniques to the classical problem of computing a ball-carving with strong-diameter $O(\log^2 n)$ in an unweighted graph, our new clustering algorithm still leads to an improvement in round complexity from $O(\log^{10} n)$ rounds [Chang, Ghaffari PODC 21] to $O(\log^{4} n)$.