Year: 2025
Type: preprint
Abstract: Graph modification problems with the goal of optimizing some measure of a given node's network position have a rich history in the algorithms literature. Less commonly explored are modification problems with the goal of equalizing positions, though this class of problems is well-motivated from the perspective of equalizing social capital, i.e., algorithmic fairness. In this work, we study how to add edges to make the closeness centralities of a given pair of nodes more equal. We formalize several versions of this problem: Closeness Ratio Improvement, which aims to maximize the ratio of closeness centralities between two specified nodes, and Closeness Gap Minimization, which aims to minimize the absolute difference of centralities. For the former, we present a quasilinear-time $\frac{6}{11}$-approximation, complemented by a bicriteria inapproximability bound. In contrast to this positive result, we show that Closeness Gap Minimization admits no multiplicative approximation, unless P=NP. We also establish NP-hardness for All-Pairs Closeness Ratio Improvement, which aims to maximize the minimum ratio of closeness centralities across all node pairs.
Source: arXiv (Cornell University)
Institutions: University of Utah, Haverford College
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4417088159
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