Fairness-Constrained Influence Maximisation via Multi-Objective Optimisation

Abstract

The Influence Maximisation (IM) problem seeks to select a set of seed nodes to maximise information diffusion in a network. While existing approaches have achieved significant improvements in overall diffusion, they often overlook fairness across communities, which can result in biased dissemination and the exclusion of disadvantaged groups. To address this, we define the Fair Multi-objective Influence Maximisation (FMOIM) problem, which jointly optimises influence spread and equity fairness. Equity Fairness is modelled at the community level as the alignment between the realised diffusion-benefit distribution and a desired reference allocation. Jensen–Shannon divergence (JSD) similarity quantifies distributional deviation from the reference allocation, while Jain’s fairness index characterises the evenness of benefit allocation across communities. To solve FMOIM, FairWolf is proposed as a problem-driven discrete multi-objective optimisation model for fairness-aware influence maximisation. It reformulates the Grey Wolf Optimiser dynamics to search directly over fixed-budget seed sets under community-level fairness objectives, capturing the spread and fairness trade-off. FairWolf incorporates three components: (i) a discrete position-updating mechanism tailored to seed-set construction, (ii) an Explorer-Augmented Leader Selection strategy that enhances population diversity while maintaining convergence pressure, and (iii) a Hypervolume (HV)-triggered perturbation mechanism that adaptively mitigates stagnation in non-convex multi-objective search spaces. Experiments on eight real-world networks demonstrate that the FairWolf model consistently outperforms state-of-the-art baselines, yielding a higher HV value and more uniformly distributed Pareto fronts. These results demonstrate its effectiveness and practicality for fairness-aware diffusion in applications such as viral marketing, public health, and resource allocation.