Strong transitivity of a graph

TL;DR

This paper introduces the concept of strong transitive partitions in graphs, studies their computational complexity, and provides efficient algorithms for trees and split graphs, while proving NP-completeness for chordal graphs.

Contribution

It defines strong transitive partitions, analyzes the maximum strong transitivity problem, and establishes complexity results with algorithms for specific graph classes.

Findings

NP-complete for chordal graphs

Linear-time algorithm for trees

Linear-time algorithm for split graphs

Abstract

A vertex partition $\pi = \{V_1, V_2, \ldots, V_k\}$ of $G$ is called a \emph{transitive partition} of size $k$ if $V_i$ dominates $V_j$ for all $1\leq i<j\leq k$. For two disjoint subsets $A$ and $B$ of $V$, we say $A$ \emph{strongly dominates} $B$ if for every vertex $y\in B$, there exists a vertex $x\in A$, such that $xy\in E$ and $deg_G(x)\geq deg_G(y)$. A vertex partition $\pi = \{V_1, V_2, \ldots, V_k\}$ of $G$ is called a \emph{strong transitive partition} of size $k$ if $V_i$ strongly dominates $V_j$ for all $1\leq i<j\leq k$. The \textsc{Maximum Strong Transitivity Problem} is to find a strong transitive partition of a given graph with the maximum number of parts. In this article, we initiate the study of this variation of transitive partition from algorithmic point of view. We show that the decision version of this problem is NP-complete for chordal graphs. On the positive side, we prove that this problem can be solved in linear time for trees and split graphs.