Hardness of the Generalized Coloring Numbers
Michael Breen-McKay, Brian Lavallee, Blair D. Sullivan

TL;DR
This paper investigates the computational hardness of generalized coloring numbers, proving NP-hardness and para-NP-hardness results, and introduces improved approximation algorithms for these graph parameters.
Contribution
It establishes the NP-hardness and para-NP-hardness of computing generalized coloring numbers and provides improved approximation algorithms with better runtime and approximation guarantees.
Findings
Computing weak 2-coloring number is NP-hard.
Determining if a graph has weak r-coloring number at most k is para-NP-hard for all r ≥ 2.
A greedy ordering algorithm achieves a (k-1)^{r-1}-approximation for r-coloring number.
Abstract
The generalized coloring numbers of Kierstead and Yang (Order 2003) offer an algorithmically-useful characterization of graph classes with bounded expansion. In this work, we consider the hardness and approximability of these parameters. First, we complete the work of Grohe et al. (WG 2015) by showing that computing the weak 2-coloring number is NP-hard. Our approach further establishes that determining if a graph has weak -coloring number at most is para-NP-hard when parameterized by for all . We adapt this to determining if a graph has -coloring number at most as well, proving para-NP-hardness for all . Para-NP-hardness implies that no XP algorithm (runtime ) exists for testing if a generalized coloring number is at most . Moreover, there exists a constant such that it is NP-hard to approximate the generalized coloring numbers…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Advanced Graph Theory Research · Machine Learning and Algorithms
