Optimal Data Reduction for Graph Coloring Using Low-Degree Polynomials
Bart M. P. Jansen, Astrid Pieterse

TL;DR
This paper advances graph coloring data reduction by providing optimal kernel sizes for q-Coloring parameterized by vertex cover and twin-cover, and proves non-existence of small kernels for 3-Coloring under certain complexity assumptions.
Contribution
It establishes optimal kernel bounds for q-Coloring with vertex cover and twin-cover parameters, and shows 3-Coloring cannot be sparsified below certain sizes assuming standard complexity hypotheses.
Findings
Kernel of size O(k^{q-1} log k) for q-Coloring parameterized by Vertex Cover.
Kernel of size O(k^{Δ(H)} log k) for H-Coloring parameterized by Twin-Cover.
No polynomial-size kernel for 3-Coloring parameterized by number of vertices, assuming NP not in coNP/poly.
Abstract
The theory of kernelization can be used to rigorously analyze data reduction for graph coloring problems. Here, the aim is to reduce a q-Coloring input to an equivalent but smaller input whose size is provably bounded in terms of structural properties, such as the size of a minimum vertex cover. In this paper we settle two open problems about data reduction for q-Coloring. First, we obtain a kernel of bitsize for q-Coloring parameterized by Vertex Cover, for any q >= 3. This size bound is optimal up to factors assuming NP is not a subset of coNP/poly, and improves on the previous-best kernel of size . We generalize this result for deciding q-colorability of a graph G, to deciding the existence of a homomorphism from G to an arbitrary fixed graph H. Furthermore, we can replace the parameter vertex cover by the less restrictive parameter…
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