Fast Deterministic Distributed Degree Splitting

TL;DR

This paper introduces improved algorithms for balanced orientations and degree splits in distributed networks, achieving faster runtimes and extending to edge coloring applications.

Contribution

It presents a new $ ilde{O}(rac{1}{ ext{epsilon}} imes ext{log} n)$ algorithm for degree splitting with small discrepancy, improving prior work, and extends this to edge coloring.

Findings

Achieves $ ilde{O}(rac{1}{ ext{epsilon}} imes ext{log} n)$ complexity for degree splitting.

Extends results to undirected degree splits with same runtime.

Provides a faster $(3/2 + ext{epsilon}) ext{Delta}$-edge coloring algorithm.

Abstract

We obtain better algorithms for computing more balanced orientations and degree splits in LOCAL. Important to our result is a connection to the hypergraph sinkless orientation problem [BMNSU, SODA'25] We design an algorithm of complexity $\mathcal{O}(\varepsilon^{-1} \cdot \log n)$ for computing a balanced orientation with discrepancy at most $\varepsilon \cdot \mathrm{deg}(v)$ for every vertex $v \in V$. This improves upon a previous result by [GHKMSU, Distrib. Comput. 2020] of complexity $\mathcal{O}(\varepsilon^{-1} \cdot \log \varepsilon^{-1} \cdot (\log \log \varepsilon^{-1})^{1.71} \cdot \log n)$. Further, we show that this result can also be extended to compute undirected degree splits with the same discrepancy and in the same runtime. As as application we show that $(3 / 2 + \varepsilon)\Delta$-edge coloring can now be solved in $\mathcal{O}(\varepsilon^{-1} \cdot \log^2 \Delta \cdot \log n + \varepsilon^{-2} \cdot \log n)$ rounds in LOCAL. Note that for constant $\varepsilon$ and $\Delta = \mathcal{O}(2^{\log^{1/3} n})$ this runtime matches the current state-of-the-art for $(2\Delta - 1)$-edge coloring in [Ghaffari & Kuhn, FOCS'21].