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Palette Sparsification for Graphs with Sparse Neighborhoods

Abhishek Dhawan

TL;DR

A key element in the proof is a proposition regarding correspondence coloring in the so-called color-degree setting, which improves upon recent work of Anderson, Kuchukova, and the author and is of independent interest.

Abstract

A seminal palette sparsification result of Assadi, Chen, and Khanna states that in every $n$-vertex graph of maximum degree $Δ$, sampling $Θ(\log n)$ colors per vertex from $\{1, \ldots, Δ+1\}$ almost certainly allows for a proper coloring from the sampled colors. Alon and Assadi extended this work proving a similar result for $O\left(Δ/\log Δ\right)$-coloring triangle-free graphs. Apart from being interesting results from a combinatorial standpoint, their results have various applications to the design of graph coloring algorithms in different models of computation. In this work, we focus on locally sparse graphs, i.e., graphs with sparse neighborhoods. We say a graph $G = (V, E)$ is $k$-locally-sparse if for each vertex $v \in V$, the subgraph $G[N(v)]$ contains at most $k$ edges. A celebrated result of Alon, Krivelevich, and Sudakov shows that such graphs are $O(Δ/\log (Δ/\sqrt{k}))$-colorable. For any $α\in (0, 1)$ and $k \ll Δ^{2α}$, let $G$ be a $k$-locally-sparse graph. For $q = Θ\left(Δ/\log \left(Δ^α/\sqrt{k}\right)\right)$, we show that sampling $O\left(Δ^α+ \sqrt{\log n}\right)$ colors per vertex is sufficient to obtain a proper $q$-coloring of $G$ from the sampled colors. Setting $k = 1$ recovers the aforementioned result of Alon and Assadi for triangle-free graphs. A key element in our proof is a proposition regarding correspondence coloring in the so-called color-degree setting, which improves upon recent work of Anderson, Kuchukova, and the author and is of independent interest.

Palette Sparsification for Graphs with Sparse Neighborhoods

TL;DR

A key element in the proof is a proposition regarding correspondence coloring in the so-called color-degree setting, which improves upon recent work of Anderson, Kuchukova, and the author and is of independent interest.

Abstract

A seminal palette sparsification result of Assadi, Chen, and Khanna states that in every -vertex graph of maximum degree , sampling colors per vertex from almost certainly allows for a proper coloring from the sampled colors. Alon and Assadi extended this work proving a similar result for -coloring triangle-free graphs. Apart from being interesting results from a combinatorial standpoint, their results have various applications to the design of graph coloring algorithms in different models of computation. In this work, we focus on locally sparse graphs, i.e., graphs with sparse neighborhoods. We say a graph is -locally-sparse if for each vertex , the subgraph contains at most edges. A celebrated result of Alon, Krivelevich, and Sudakov shows that such graphs are -colorable. For any and , let be a -locally-sparse graph. For , we show that sampling colors per vertex is sufficient to obtain a proper -coloring of from the sampled colors. Setting recovers the aforementioned result of Alon and Assadi for triangle-free graphs. A key element in our proof is a proposition regarding correspondence coloring in the so-called color-degree setting, which improves upon recent work of Anderson, Kuchukova, and the author and is of independent interest.
Paper Structure (14 sections, 28 theorems, 72 equations, 1 algorithm)

This paper contains 14 sections, 28 theorems, 72 equations, 1 algorithm.

Key Result

Theorem 1.1

Let $G = (V, E)$ be an $n$-vertex graph of maximum degree $\Delta$. Define a list assignment $L$ for $G$ by sampling $L(v)$ independently for each vertex $v \in V$ from the $\Theta(\log n)$-size subsets of $[\Delta + 1]$. Then, $G$ admits a proper $L$-coloring with high probabilityThroughout this pa

Theorems & Definitions (44)

  • Definition 1.1: List Coloring
  • Theorem 1.1: assadi2019sublinear
  • Theorem 1.2: alon2020palette
  • Theorem 1.3: anderson2022coloring
  • Definition 1.2: Local Sparsity
  • Theorem 1.4
  • Proposition 1.5
  • Proposition 1.6
  • Theorem 1.7: dhawan2024bounds
  • Definition 2.1: Correspondence Cover
  • ...and 34 more