Fair Influence Maximization in Hypergraphs

2026-06-12

AI Summary

This work addresses the issue of uneven influence distribution and lack of fairness in existing influence maximization methods when applied to hypergraphs with community structures. It pioneers the extension of fair influence maximization to hypergraph settings by proposing FIMH, a parameter-free algorithm that jointly optimizes overall influence spread and inter-community fairness. Built upon the SICP diffusion model, FIMH integrates a heuristic seed selection strategy with an ideal-point distance criterion, eliminating the need for parameter tuning. Experimental results on seven real-world hypergraph datasets demonstrate that FIMH achieves influence coverage comparable to state-of-the-art methods while significantly reducing disparity in influence across communities.

Abstract

The influence maximization problem aims to select a set of seed nodes that maximize the influence, i.e., the average number of influenced nodes, at the end of a spreading process. It has been widely studied with applications in viral marketing, public health campaigns, and social influence. In networks with pronounced community structure, existing approaches often yield an uneven distribution of influenced nodes across communities, which is unfair. Although the fair influence maximization (FIM) problem has been studied for pairwise networks, it remains largely unexplored for hyper graphs, which more accurately represent real-world systems involving group interactions. We introduce FIMH, a heuristic seed-selection algorithm for FIM on hyper graphs, under the Susceptible-Infected Contact Process (SICP) spreading model. FIMH iteratively estimates the contribution of each candidate node to influence and fairness and selects the node that best trades off these two objectives as an additional seed using a parameter-free utopia-distance criterion. Experiments on seven real-world hypergraphs demonstrate that FIMH achieves an influence comparable to that of state-of-the-art IM methods while significantly reducing influence disparity. Analysis of the topological properties of the selected seed nodes and their contributions to influence and fairness further supports the effectiveness of FIMH.

Problem

• Fair Influence Maximization
• Hypergraphs
• Influence Disparity
• Community Structure
• Group Interactions

Innovation

• Fair Influence Maximization
• Hypergraphs
• Utopia-distance criterion
• SICP spreading model
• Seed selection