We study fairness in social influence maximization, whereby one seeks to select seeds that spread a given information throughout a network, ensuring balanced outreach among different communities (e.g. demographic groups). In the literature, fairness is often quantified in terms of the expected outreach within individual communities. In this paper, we demonstrate that such fairness metrics can be misleading since they overlook the stochastic nature of information diffusion processes. When information diffusion occurs in a probabilistic manner, multiple outreach scenarios can occur. As such, outcomes such as ``In 50\% of the cases, no one in group 1 gets the information, while everyone in group 2 does, and in the other 50%, it is the opposite'', which always results in largely unfair outcomes, are classified as fair by a variety of fairness metrics in the literature. We tackle this problem by designing a new fairness metric, mutual fairness, that captures variability in outreach through optimal transport theory. We propose a new seed-selection algorithm that optimizes both outreach and mutual fairness, and we show its efficacy on several real datasets. We find that our algorithm increases fairness with only a minor decrease (and at times, even an increase) in efficiency.

翻译：本研究探讨社会影响力最大化中的公平性问题，即通过选择种子节点使特定信息在网络中传播，并确保不同社群（如人口统计群体）间获得均衡的信息覆盖。现有文献通常以各社群内部期望信息覆盖率作为公平性量化指标。本文指出此类公平性度量指标存在误导性，因其忽视了信息传播过程的随机性本质。当信息以概率方式扩散时，可能出现多种覆盖场景。例如"在50%的情况下，群体1无人获得信息而群体2全员获得；在另外50%的情况下则完全相反"这类始终导致极端不公平结果的场景，却被现有文献中的多种公平性指标判定为公平。为解决该问题，我们基于最优传输理论设计了一种能捕捉覆盖变异性的新型公平性度量指标——互公平性。我们提出了一种同时优化信息覆盖率和互公平性的新型种子选择算法，并在多个真实数据集上验证了其有效性。实验表明，该算法能在仅轻微降低（有时甚至提升）传播效率的前提下显著提升公平性。