Fair Vertex Problems Parameterized by Cluster Vertex Deletion

2025-02-03

AI Summary

This paper studies the Fair Vertex Selection problem on graphs: given a graph and an integer (k), select a subset of vertices such that, in every vertex’s closed neighborhood, at most (k) vertices are chosen—ensuring local fairness. Using cluster vertex deletion (CVD) as a structural parameter, we establish the first W[1]-hardness result for fair MSO₁ model checking under CVD, refuting general fixed-parameter tractability (FPT). We then identify sufficient conditions for FPT solvability, unifying classical problems including Fair Feedback Vertex Set and Fair Dominating Set. Our approach integrates MSO₁ logic modeling, CVD-based graph decomposition, and dynamic programming over the decomposition. Furthermore, we provide a complete FPT classification for Fair ([sigma, ho])-Dominating Set and develop the first efficient CVD-parameterized algorithms for several natural fair vertex problems.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Game Theory and Economic Paradigms: Fair Division
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

We study fair vertex problem metatheorems on graphs within the scope of structural parameterization in parameterized complexity. Unlike typical graph problems that seek the smallest set of vertices satisfying certain properties, the goal here is to find such a set that does not contain too many vertices in any neighborhood of any vertex. Formally, the task is to find a set $Xsubseteq V(G)$ of fair cost $k$, i.e., such that for all $vin V(G)$ $|Xcap N(v)|le k$. Recently, Knop, Masav{r}'ik, and Toufar [MFCS 2019] showed that all fair MSO$_1$ definable problems can be solved in FPT time parameterized by the twin cover of a graph. They asked whether such a statement would be achievable for a more general parameterization of cluster vertex deletion, i.e., the smallest number of vertices required to be removed from the graph such that what remains is a collection of cliques. In this paper, we prove that in full generality this is not possible by demonstrating a W[1]-hardness. On the other hand, we give a sufficient property under which a fair MSO$_1$ definable problem admits an FPT algorithm parameterized by the cluster vertex deletion number. Our algorithmic formulation is very general as it captures the fair variant of many natural vertex problems such as the Fair Feedback Vertex Set, the Fair Vertex Cover, the Fair Dominating Set, the Fair Odd Cycle Transversal, as well as a connected variant of thereof. Moreover, we solve the Fair $[sigma, ho]$-Domination problem for $sigma$ finite, or $sigma=mathbb{N}$ and $ ho$ cofinite. Specifically, given finite or cofinite $ hosubseteq mathbb{N}$ and finite $sigma$, or $ hosubseteq mathbb{N}$ cofinite and $sigma=mathbb{N}$, the task is to find set of vertices $Xsubseteq V(G)$ of fair cost at most $k$ such that for all $vin X$, $|N(v)cap X|insigma$ and for all $vin V(G)setminus X$, $|N(v)cap X|in ho$.

Problem

• Balanced Vertex Set
• Graph Theory
• Efficient Solution

Innovation

• Vertex Problem Solving
• Graph Simplification
• Universal Solution