Fairness-Constrained Influence Maximisation via Multi-Objective Optimisation
Date
Authors
Zhao, Ziying
Li, Weihua
Ma, Jing
Jiang, Jianhua
Bai, Quan
Gu, Wen
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier BV
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.Description
Keywords
0801 Artificial Intelligence and Image Processing, 0802 Computation Theory and Mathematics, 4602 Artificial intelligence, FairWolf, Fair influence maximisation, Grey wolf optimiser, Multi-objective optimisation, Social networks
Source
Swarm and Evolutionary Computation, ISSN: 2210-6502 (Print), Elsevier BV, 106, 102428-102428. doi: 10.1016/j.swevo.2026.102428
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© 2026 The Authors. Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article.