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Distributed $(Δ+1)$-Coloring in Graphs of Bounded Neighbo...
[Submitted on 24 Oct 2025 (v1), last revised 2 Sep 2026 (this ve · 2025-10-24 · via cs.DC updates on arXiv.org

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Abstract:The distributed coloring problem is arguably one of the key problems studied in the area of distributed graph algorithms. The most standard variant of the problem asks for a proper vertex coloring of a graph with $\Delta + 1$ colors, where $\Delta$ is the maximum degree of the graph. Despite an immense amount of work on distributed coloring problems in the distributed setting, determining the deterministic complexity of $(\Delta + 1)$-coloring in the standard message passing model remains one of the most important open questions of the area. In this paper, we aim to improve our understanding of the deterministic complexity of $(\Delta + 1)$-coloring as a function of $\Delta$ in a special family of graphs for which significantly faster algorithms are already known. The neighborhood independence $\theta$ of a graph is the maximum number of pairwise non-adjacent neighbors of some node of the graph. In general, in graphs of neighborhood independence $\theta = O(1)$ (e.g., line graphs), it is known that $(\Delta + 1)$-coloring can be solved in $2^{O(\sqrt{\log \Delta})} + O(\log^* n)$ rounds. In the present paper, we significantly improve this result, and we show that in graphs of bounded neighborhood independence, a $(\Delta + 1)$-coloring can be computed in $(\log \Delta)^{O(\log \log \Delta / \log \log \log \Delta)} + O(\log^* n)$ rounds and thus in quasipolylogarithmic time in $\Delta$. Additionally, we show that when $\theta = o(\Delta^{1/8})$, one can color the graph in $o(\sqrt{\Delta}) + O(\log^* n)$ rounds, which is faster than the current best known coloring algorithms do on general graphs. We also show that the known approach that leads to a polylogarithmic in $\Delta$ algorithm for $(2\Delta - 1)$-edge coloring already fails for edge colorings of hypergraphs of rank at least 3.

Submission history

From: Marc Fuchs [view email]
[v1] Fri, 24 Oct 2025 15:08:23 UTC (42 KB)
[v2] Sat, 2 May 2026 10:33:47 UTC (39 KB)
[v3] Wed, 2 Sep 2026 14:25:24 UTC (42 KB)