Deterministic Dynamic Edge Colouring

TL;DR

This paper presents a deterministic dynamic edge colouring algorithm that efficiently maintains near-optimal colourings in graphs undergoing edge insertions and deletions, improving over previous randomized methods.

Contribution

It introduces the first deterministic algorithm achieving sub-polynomial update time for maintaining a near-optimal edge colouring below the greedy threshold.

Findings

Deterministic algorithm with sub-polynomial amortised update time.

Maintains a $(1+oldsymbol{ m extit{ extvarepsilon}})oldsymbol{ m extit{ extDelta}}$-edge-colouring.

First deterministic method to beat the $2oldsymbol{ m extit{ extDelta}}-1$ colour threshold.

Abstract

Given a dynamic graph $G$ with $n$ vertices and $m$ edges subject to insertion an deletions of edges, we show how to maintain a $(1+\varepsilon)\Delta$-edge-colouring of $G$ without the use of randomisation. More specifically, we show a deterministic dynamic algorithm with an amortised update time of $2^{\tilde{O}_{\log \varepsilon^{-1}}(\sqrt{\log n})}$ using $(1+\varepsilon)\Delta$ colours. If $\varepsilon^{-1} \in 2^{O(\log^{0.49} n)}$, then our update time is sub-polynomial in $n$. While there exists randomised algorithms maintaining colourings with the same number of colours [Christiansen STOC'23, Duan, He, Zhang SODA'19, Bhattacarya, Costa, Panski, Solomon SODA'24] in polylogarithmic and even constant update time, this is the first deterministic algorithm to go below the greedy threshold of $2\Delta-1$ colours for all input graphs. On the way to our main result, we show how to dynamically maintain a shallow hierarchy of degree-splitters with both recourse and update time in $n^{o(1)}$. We believe that this algorithm might be of independent interest.