Fair, Efficient and Connected Allocations on Graphs

2026-08-19

AI Summary

研究图上公平、高效且连通的分配问题，通过平等和功利主义福利最大化衡量效率，探讨了经典及参数化复杂性。

Abstract

We study the classical and parameterized complexity of efficient connected allocation problems on graphs, where efficiency is measured by egalitarian and utilitarian welfare maximization. We first establish a sharp complexity dichotomy in the classical setting: both problems are NP-hard in general and remain hard even on very restricted graph classes such as paths, and consequently trees and cycles. In contrast, they are polynomial-time solvable on stars, but this tractability does not extend even to the case of two disjoint stars. Motivated by these boundaries, we move to the parameterized complexity framework, where we study the problem with respect to the number of agents. We obtain fixed-parameter tractability (FPT) on trees and, more generally, identify a robust phenomenon whereby tractability on a connected graph class extends to disjoint unions of graphs from that class. We further investigate the parameters treewidth and treedepth, showing that the utilitarian version is FPT for both, whereas the egalitarian version remains para-NP-hard even on graphs of treedepth two. Finally, we analyze the number of connected components and show that except for the collection of stars, the problems remain hard. For the collection of stars,while we obtain para-NP-hardness for the egalitarian case, the utilitarian case gives W[2]-hardness together with an XP algorithm.

Problem

• efficient connected allocation
• egalitarian welfare
• utilitarian welfare
• parameterized complexity
• graph classes

Innovation

• parameterized complexity
• fixed-parameter tractability (FPT)
• egalitarian welfare
• utilitarian welfare
• treewidth