Density-Sensitive Algorithms for $(\Delta + 1)$-Edge Coloring

TL;DR

This paper introduces a new $( abla + 1)$-edge coloring algorithm that adapts to graph density, significantly improving runtime for graphs with low arboricity or when arboricity is much less than maximum degree.

Contribution

It presents a density-sensitive algorithm that reduces the running time of $( abla + 1)$-edge coloring by a factor of $rac{ ext{arboricity}}{ ext{max degree}}$, improving previous bounds.

Findings

Achieves near-linear runtime for bounded arboricity graphs.

Improves existing algorithms by a factor of $rac{ ext{arboricity}}{ ext{max degree}}$.

Builds on Sinnamon's algorithm with a density-sensitive refinement.

Abstract

Vizing's theorem asserts the existence of a $(\Delta+1)$-edge coloring for any graph $G$, where $\Delta = \Delta(G)$ denotes the maximum degree of $G$. Several polynomial time $(\Delta+1)$-edge coloring algorithms are known, and the state-of-the-art running time (up to polylogarithmic factors) is $\tilde{O}(\min\{m \cdot \sqrt{n}, m \cdot \Delta\})$, by Gabow et al.\ from 1985, where $n$ and $m$ denote the number of vertices and edges in the graph, respectively. (The $\tilde{O}$ notation suppresses polylogarithmic factors.) Recently, Sinnamon shaved off a polylogarithmic factor from the time bound of Gabow et al. The {arboricity} $\alpha = \alpha(G)$ of a graph $G$ is the minimum number of edge-disjoint forests into which its edge set can be partitioned, and it is a measure of the graph's "uniform density". While $\alpha \le \Delta$ in any graph, many natural and real-world graphs exhibit a significant separation between $\alpha$ and $\Delta$. In this work we design a $(\Delta+1)$-edge coloring algorithm with a running time of $\tilde{O}(\min\{m \cdot \sqrt{n}, m \cdot \Delta\})\cdot \frac{\alpha}{\Delta}$, thus improving the longstanding time barrier by a factor of $\frac{\alpha}{\Delta}$. In particular, we achieve a near-linear runtime for bounded arboricity graphs (i.e., $\alpha = \tilde{O}(1)$) as well as when $\alpha = \tilde{O}(\frac{\Delta}{\sqrt{n}})$. Our algorithm builds on Sinnamon's algorithm, and can be viewed as a density-sensitive refinement of it.