Algorithmic Fairness for Influence Maximization: A Survey of Models, Metrics, and Optimization Techniques

Abstract

Influence maximization (IM) has become a basis of modeling information spread across social networks, with applications in viral marketing, public health, and political campaigns. However, classical IM algorithms often prioritize the spread of overall influence and ignore its fairness, resulting in disadvantages for marginalized or underrepresented communities. This survey provides a detailed look at fairness-aware influence maximization, focusing on important challenges, models, and methods designed to ensure fair influence maximization. We categorize existing methods into five groups based on their fairness objectives, including parity, diversity, and utility, and discuss how they handle biased network structure, protected attributes, and seeding constraints. We also examine how fairness-aware influence maximization methods perform on real-world data to highlight the importance of combining fairness with classical influence maximization methods. By bridging the gap between influence spread and social equity, this survey lays the foundation for designing fair algorithms that ensure fair representation in large-scale diffusion processes. The survey concludes by outlining emerging trends, open challenges, and prospective future directions in the domain, highlighting the need for expertise spanning multiple disciplines.