Improving Fairness in Information Exposure by Adding Links
Ruben Becker, Gianlorenzo D'Angelo, Sajjad Ghobadi

TL;DR
This paper explores how adding links to a network can make influence spreading more fair without explicitly optimizing for fairness, combining theoretical analysis with practical heuristics and experiments.
Contribution
It introduces a novel approach of modifying network structure to achieve fairness in influence spread, with theoretical hardness results and effective heuristics demonstrated experimentally.
Findings
Adding few edges can improve ex-post fairness significantly.
Heuristics outperform fairness-specific algorithms in practice.
Approach achieves fairness comparable to or better than existing methods.
Abstract
Fairness in influence maximization has been a very active research topic recently. Most works in this context study the question of how to find seeding strategies (deterministic or probabilistic) such that nodes or communities in the network get their fair share of coverage. Different fairness criteria have been used in this context. All these works assume that the entity that is spreading the information has an inherent interest in spreading the information fairly, otherwise why would they want to use the developed fair algorithms? This assumption may however be flawed in reality -- the spreading entity may be purely \emph{efficiency-oriented}. In this paper we propose to study two optimization problems with the goal to modify the network structure by adding links in such a way that efficiency-oriented information spreading becomes \emph{automatically fair}. We study the proposed…
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Taxonomy
TopicsGame Theory and Applications · Experimental Behavioral Economics Studies · Game Theory and Voting Systems
