# Fairness in Influence Maximization through Randomization

arXiv: https://arxiv.org/abs/2010.03438 · HTML: https://mlsanity.com/paper/2010.03438/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time](https://mlsanity.com/paper/2403.18755/) (2024)
- [A Pressure-Based Diffusion Model for Influence Maximization on Social Networks](https://mlsanity.com/paper/2509.12822/) (2025)
- [DQ4FairIM: Fairness-aware Influence Maximization using Deep Reinforcement Learning](https://mlsanity.com/paper/2512.00545/) (2025)
- [Fair Information Spread on Social Networks with Community Structure](https://mlsanity.com/paper/2305.08791/) (2023)

## Abstract (arXiv)

The influence maximization paradigm has been used by researchers in various fields in order to study how information spreads in social networks. While previously the attention was mostly on efficiency, more recently fairness issues have been taken into account in this scope. In this paper, we propose to use randomization as a mean for achieving fairness. Similar to previous works like Fish et al. (WWW '19) and Tsang et al. (IJCAI '19), we study the maximin criterion for (group) fairness. In contrast to their work however, we model the problem in such a way that, when choosing the seed sets, probabilistic strategies are possible rather than only deterministic ones. We introduce two different variants of this probabilistic problem, one that entails probabilistic strategies over nodes (node-based problem) and a second one that entails probabilistic strategies over sets of nodes (set-based problem). While the original deterministic problem involving the maximin criterion has been shown to be inapproximable, interestingly, we show that both probabilistic variants permit approximation algorithms that achieve a constant multiplicative factor of 1-1/e plus an additive arbitrarily small error that is due to the simulation of the information spread. For an experimental study, we provide implementations of multiplicative-weight routines for both problems and compare the achieved fairness values to existing methods. Maybe non-surprisingly, we show that the ex-ante values of the computed probabilistic strategies are significantly larger than the (ex-post) fairness values of previous methods. This indicates that studying fairness via randomization is a worthwhile path to follow. Interestingly and maybe more surprisingly, we observe that even the ex-post fairness values computed by our routines, dominate over the fairness achieved by previous methods on most of the instances tested.