English

Coloring 3-Colorable Graphs with Low Threshold Rank

Data Structures and Algorithms 2025-08-06 v1

Abstract

We present a new algorithm for finding large independent sets in 33-colorable graphs with small 11-sided threshold rank. Specifically, given an nn-vertex 33-colorable graph whose uniform random walk matrix has at most rr eigenvalues larger than ε\varepsilon, our algorithm finds a proper 33-coloring on at least (12O(ε))n(\frac{1}{2}-O(\varepsilon))n vertices in time nO(r/ε2)n^{O(r/\varepsilon^2)}. This extends and improves upon the result of Bafna, Hsieh, and Kothari on 11-sided expanders. Furthermore, an independent work by Buhai, Hua, Steurer, and V\'ari-Kakas shows that it is UG-hard to properly 33-color more than (12+ε)n(\frac{1}{2}+\varepsilon)n vertices, thus establishing the tightness of our result. Our proof is short and simple, relying on the observation that for any distribution over proper 33-colorings, the correlation across an edge must be large if the marginals of the endpoints are not concentrated on any single color. Notably, this property fails for 44-colorings, which is consistent with the hardness result of [BHK25] for 44-colorable 11-sided expanders.

Keywords

Cite

@article{arxiv.2508.03093,
  title  = {Coloring 3-Colorable Graphs with Low Threshold Rank},
  author = {Jun-Ting Hsieh},
  journal= {arXiv preprint arXiv:2508.03093},
  year   = {2025}
}
R2 v1 2026-07-01T04:34:32.555Z