# Group-Fairness in Influence Maximization

## TL;DR

This paper introduces formal fairness definitions in influence maximization, proposes algorithms to ensure fair influence spread across groups, and demonstrates their effectiveness on real-world social network data.

## Contribution

It provides a novel fairness framework for influence maximization and develops algorithms that balance influence spread with fairness constraints.

## Key findings

- Fair algorithms significantly reduce disparities among groups.
- Standard methods often neglect smaller groups, leading to unfair outcomes.
- Proposed methods improve fairness without substantial utility loss.

## Abstract

Influence maximization is a widely used model for information dissemination in social networks. Recent work has employed such interventions across a wide range of social problems, spanning public health, substance abuse, and international development (to name a few examples). A critical but understudied question is whether the benefits of such interventions are fairly distributed across different groups in the population; e.g., avoiding discrimination with respect to sensitive attributes such as race or gender. Drawing on legal and game-theoretic concepts, we introduce formal definitions of fairness in influence maximization. We provide an algorithmic framework to find solutions which satisfy fairness constraints, and in the process improve the state of the art for general multi-objective submodular maximization problems. Experimental results on real data from an HIV prevention intervention for homeless youth show that standard influence maximization techniques oftentimes neglect smaller groups which contribute less to overall utility, resulting in a disparity which our proposed algorithms substantially reduce.