Matching (Multi)Cut: Algorithms, Complexity, and Enumeration
Guilherme C. M. Gomes, Emanuel Juliano, Gabriel Martins and, Vinicius F. dos Santos

TL;DR
This paper introduces and analyzes the Matching Multicut problem, exploring its computational complexity, kernelization, and enumeration algorithms across various graph parameters, extending the classical Matching Cut problem.
Contribution
It generalizes Matching Cut to Matching Multicut, providing complexity results, kernelization bounds, and enumeration algorithms for different graph classes and parameters.
Findings
NP-hardness on cubic graphs for Matching Multicut
Existence of a quasi-linear kernel when parameterized by
An exponential time algorithm with complexity O(^{n/2})
Abstract
A matching cut of a graph is a partition of its vertex set in two such that no vertex has more than one neighbor across the cut. The Matching Cut problem asks if a graph has a matching cut. This problem, and its generalization d-cut, has drawn considerable attention of the algorithms and complexity community in the last decade, becoming a canonical example for parameterized enumeration algorithms and kernelization. In this paper, we introduce and study a generalization of Matching Cut, which we have named Matching Multicut: can we partition the vertex set of a graph in at least parts such that no vertex has more than one neighbor outside its part? We investigate this question in several settings. We start by showing that, contrary to Matching Cut, it is NP-hard on cubic graphs but that, when is a parameter, it admits a quasi-linear kernel. We also show an…
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