DQ4FairIM: Fairness-aware Influence Maximization using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2512.00545v1
• Date: Sat, 29 Nov 2025 16:31:20 GMT
• Title: DQ4FairIM: Fairness-aware Influence Maximization using Deep Reinforcement Learning
• Authors: Akrati Saxena, Harshith Kumar Yadav, Bart Rutten, Shashi Shekhar Jha,
• Abstract summary: The Influence Maximization (IM) problem aims to select a set of seed nodes within a given budget to maximize the spread of influence in a social network.<n>We propose a fairness-aware IM method using Reinforcement Learning (RL) that ensures equitable influence outreach across all communities.<n>We perform extensive experiments on synthetic benchmarks and real-world networks to compare our method with fairness-agnostic and fairness-aware baselines.
• Score: 1.3474501014756584
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The Influence Maximization (IM) problem aims to select a set of seed nodes within a given budget to maximize the spread of influence in a social network. However, real-world social networks have several structural inequalities, such as dominant majority groups and underrepresented minority groups. If these inequalities are not considered while designing IM algorithms, the outcomes might be biased, disproportionately benefiting majority groups while marginalizing minorities. In this work, we address this gap by designing a fairness-aware IM method using Reinforcement Learning (RL) that ensures equitable influence outreach across all communities, regardless of protected attributes. Fairness is incorporated using a maximin fairness objective, which prioritizes improving the outreach of the least-influenced group, pushing the solution toward an equitable influence distribution. We propose a novel fairness-aware deep RL method, called DQ4FairIM, that maximizes the expected number of influenced nodes by learning an RL policy. The learnt policy ensures that minority groups formulate the IM problem as a Markov Decision Process (MDP) and use deep Q-learning, combined with the Structure2Vec network embedding, earning together with Structure2Vec network embedding to solve the MDP. We perform extensive experiments on synthetic benchmarks and real-world networks to compare our method with fairness-agnostic and fairness-aware baselines. The results show that our method achieves a higher level of fairness while maintaining a better fairness-performance trade-off than baselines. Additionally, our approach learns effective seeding policies that generalize across problem instances without retraining, such as varying the network size or the number of seed nodes.

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