Linear Kernels for Separating a Graph into Components of Bounded Size

TL;DR

This paper introduces a linear kernelization method for the p-Size Separator problem, reducing problem size to O(pk) vertices, generalizing vertex cover kernelization techniques.

Contribution

It provides the first linear vertex kernel for the p-Size Separator problem, extending the Nemhauser-Trotter theorem to a broader class of graph separation problems.

Findings

Achieved an O(p^2k) vertex kernel using an extension of the expansion lemma.

Reduced the kernel size to O(pk) with local adjustment techniques.

The approach generalizes kernelization methods for vertex cover to other separation problems.

Abstract

Graph separation and partitioning are fundamental problems that have been extensively studied both in theory and practice. The \textsc{$p$-Size Separator} problem, closely related to the \textsc{Balanced Separator} problem, is to check whether we can delete at most $k$ vertices in a given graph $G$ such that each connected component of the remaining graph has at most $p$ vertices. This problem is NP-hard for each fixed integer $p\geq 1$ and it becomes the famous \textsc{Vertex Cover} problem when $p=1$. It is known that the problem with parameter $k$ is W[1]-hard for unfixed $p$. In this paper, we prove a kernel of $O(pk)$ vertices for this problem, i.e., a linear vertex kernel for each fixed $p \geq 1$. In fact, we first obtain an $O(p^2k)$ vertex kernel by using a nontrivial extension of the expansion lemma. Then we further reduce the kernel size to $O(pk)$ by using some `local adjustment' techniques. Our proofs are based on extremal combinatorial arguments and the main result can be regarded as a generalization of the Nemhauser and Trotter's theorem for the \textsc{Vertex Cover} problem. These techniques are possible to be used to improve kernel sizes for more problems, especially problems with kernelization algorithms based on techniques similar to the expansion lemma or crown decompositions.