Algebrisation in Distributed Graph Algorithms: Fast Matrix Multiplication in the Congested Clique

TL;DR

This paper introduces algebraic techniques, specifically fast matrix multiplication, to improve distributed graph algorithms in the congested clique model, achieving significantly faster runtimes for triangle detection and APSP problems.

Contribution

It demonstrates the novel application of algebrisation and algebraic methods to distributed graph algorithms, resulting in faster solutions than prior combinatorial approaches.

Findings

Triangle detection in O(n^{0.15715}) time

Unweighted APSP in O(n^{0.15715}) time

Weighted APSP approximation with similar speed

Abstract

While algebrisation constitutes a powerful technique in the design and analysis of centralised algorithms, to date there have been hardly any applications of algebraic techniques in the context of distributed graph algorithms. This work is a case study that demonstrates the potential of algebrisation in the distributed context. We will focus on distributed graph algorithms in the congested clique model; the graph problems that we will consider include, e.g., the triangle detection problem and the all-pairs shortest path problem (APSP). There is plenty of prior work on combinatorial algorithms in the congested clique model: for example, Dolev et al. (DISC 2012) gave an algorithm for triangle detection with a running time of $\tilde O(n^{1/3})$, and Nanongkai (STOC 2014) gave an approximation algorithm for APSP with a running time of $\tilde O(n^{1/2})$. In this work, we will use algebraic techniques -- in particular, algorithms based on fast matrix multiplication -- to solve both triangle detection and the unweighted APSP in time $O(n^{0.15715})$; for weighted APSP, we give a $(1+o(1))$-approximation with this running time, as well as an exact $\tilde O(n^{1/3})$ solution.