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.

翻译：影响力最大化问题旨在给定预算下选择一组种子节点，以最大化社交网络中影响力的传播范围。然而，现实世界中的社交网络存在多种结构性不平等，例如占主导地位的多数群体与代表性不足的少数群体。若在设计影响力最大化算法时未考虑这些不平等因素，结果可能产生偏差，使多数群体过度受益而少数群体被边缘化。本研究通过设计一种基于强化学习的公平性感知影响力最大化方法来解决这一缺陷，确保所有社区（无论其受保护属性如何）均能获得公平的影响力覆盖。公平性通过最大化最小公平目标实现，该目标优先提升影响力最弱群体的覆盖范围，推动解决方案趋向公平的影响力分布。我们提出了一种名为DQ4FairIM的新型公平性感知深度强化学习方法，通过学习强化学习策略来最大化受影响节点的期望数量。该学习策略确保少数群体将影响力最大化问题建模为马尔可夫决策过程，并采用深度Q学习结合Structure2Vec网络嵌入方法（与Structure2Vec网络嵌入协同训练）求解该决策过程。我们在合成基准数据集和真实世界网络上进行了大量实验，将本方法与忽略公平性及具备公平性感知的基线方法进行比较。结果表明，相较于基线方法，本方法在保持更优的公平性-性能权衡的同时，实现了更高水平的公平性。此外，我们的方法能够学习有效的种子选择策略，该策略无需重新训练即可泛化至不同问题实例（如网络规模或种子节点数量的变化）。