GAEA：基于强化学习的公平访问图增强
GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning
AI总结：
研究提出GAEA问题，通过预算约束下编辑图边增强网络公平性，证明其NP-hard性并开发基于MRP的高效强化学习框架，在合成与真实数据上验证了有效性。
摘要：
不同子群体对资源的差异化访问是社会和社会技术网络中普遍存在的问题。例如，城市基础设施网络可能使某些种族群体更容易访问优质学校、杂货店和投票站等资源。类似地，大学和组织内的社交网络可能使某些群体更容易接触到掌握有价值信息或影响力的人。在此，我们引入一类新问题——公平访问图增强（GAEA），旨在通过在预算约束下编辑图边来增强网络系统的公平性。我们证明此类问题是NP-hard的，且无法在$(1-\ frac{1}{3e})$因子内被近似。我们为GAEA开发了一个有原则的、样本和时间高效的基于马尔可夫奖励过程（MRP）的机制设计框架。我们的算法在多种合成图上优于基线。我们进一步将该方法应用于真实网络，通过合并芝加哥市的公共人口普查、学校和交通数据集，并应用我们的算法对公交网络找到人类可解释的编辑，以增强跨种族群体对优质学校的公平访问。在大学Facebook网络上的进一步实验产生了新的社交连接集合，这将增加跨性别群体对某些属性节点的公平访问。
英文摘要：
Disparate access to resources by different subpopulations is a prevalent issue in societal and sociotechnical networks. For example, urban infrastructure networks may enable certain racial groups to more easily access resources such as high-quality schools, grocery stores, and polling places. Similarly, social networks within universities and organizations may enable certain groups to more easily access people with valuable information or influence. Here we introduce a new class of problems, Graph Augmentation for Equitable Access (GAEA), to enhance equity in networked systems by editing graph edges under budget constraints. We prove such problems are NP-hard, and cannot be approximated within a factor of $(1-\tfrac{1}{3e})$. We develop a principled, sample- and time- efficient Markov Reward Process (MRP)-based mechanism design framework for GAEA. Our algorithm outperforms baselines on a diverse set of synthetic graphs. We further demonstrate the method on real-world networks, by merging public census, school, and transportation datasets for the city of Chicago and applying our algorithm to find human-interpretable edits to the bus network that enhance equitable access to high-quality schools across racial groups. Further experiments on Facebook networks of universities yield sets of new social connections that would increase equitable access to certain attributed nodes across gender groups.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
