公平影响力最大化：一种福利优化方法
Fair Influence Maximization: A Welfare Optimization Approach
摘要：
一些行为，社会和公共卫生干预措施，如自杀/艾滋病毒预防或社区对自然灾害的准备，利用社交网络信息，以最大限度地扩大覆盖面。已经提出了政治影响力最大化技术，以帮助在这种干预中选择“同行领导者”或“影响者”。然而，影响力最大化的传统算法并没有考虑到这些干预而设计。因此，它们可能不成比例地将少数群体社区排除在干预措施的惠益之外。这激发了对公平影响力最大化的研究。现有技术具有两个主要缺点。首先，它们需要承诺一个单一的公平措施。第二，这些措施通常作为严格的约束而施加，导致不期望的性质，例如资源浪费。为了解决这些缺点，我们提供了一个公平的影响力最大化算法应该满足的属性的原则性表征。特别是，我们提出了一个框架的基础上社会福利理论，其中每个社区的基数效用汇总使用等弹性社会福利功能。在此框架下，公平与效率之间的权衡可以通过一个单一的不平等厌恶设计参数来控制。然后，我们表明在什么情况下，我们提出的原则可以满足福利功能。由此产生的优化问题是单调和次模块化的，可以有效地解决最优性保证。我们的框架包括作为特殊情况leximin和比例公平。合成和真实世界的数据集，包括滑坡风险管理的案例研究的广泛实验证明了所提出的框架的有效性。
英文摘要：
Several behavioral, social, and public health interventions, such as suicide/HIV prevention or community preparedness against natural disasters, leverage social network information to maximize outreach. Algorithmic influence maximization techniques have been proposed to aid with the choice of "peer leaders" or "influencers" in such interventions. Yet, traditional algorithms for influence maximization have not been designed with these interventions in mind. As a result, they may disproportionately exclude minority communities from the benefits of the intervention. This has motivated research on fair influence maximization. Existing techniques come with two major drawbacks. First, they require committing to a single fairness measure. Second, these measures are typically imposed as strict constraints leading to undesirable properties such as wastage of resources. To address these shortcomings, we provide a principled characterization of the properties that a fair influence maximization algorithm should satisfy. In particular, we propose a framework based on social welfare theory, wherein the cardinal utilities derived by each community are aggregated using the isoelastic social welfare functions. Under this framework, the trade-off between fairness and efficiency can be controlled by a single inequality aversion design parameter. We then show under what circumstances our proposed principles can be satisfied by a welfare function. The resulting optimization problem is monotone and submodular and can be solved efficiently with optimality guarantees. Our framework encompasses as special cases leximin and proportional fairness. Extensive experiments on synthetic and real world datasets including a case study on landslide risk management demonstrate the efficacy of the proposed framework.
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