提出公平性模块度，让社区划分自动兼顾公平与质量
Recovering Fairness Directly from Modularity: a New Way for Fair Community Partitioning
- 引入受保护群体网络，设计同时优化公平与模块度的新指标
- 在不平衡数据集上，公平性提升显著且分区质量高
- 适合关注算法公平性的社会网络分析研究者
社区划分在图分析中至关重要，传统模块度优化常忽视公平性这一现实应用关键因素。为此，本文提出受保护群体网络，并构建新的公平-模块度度量，该度量在传统模块度基础上显式纳入公平性约束。理论证明，最小化此度量可自然获得对受保护群体公平的划分结果，同时保持理论完备性。本文还构建通用优化框架，并设计高效算法 Fair Fast Newman (FairFN)，在改进 Fast Newman (FN) 的基础上实现模块度与公平性的联合优化。实验表明，相比现有最优方法，FairFN 在不平衡数据集上显著提升了公平性表现，同时维持高质量社区划分。
原文摘要 · Abstract (English)
Community partitioning is crucial in network analysis, with modularity optimization being the prevailing technique. However, traditional modularity-based methods often overlook fairness, a critical aspect in real-world applications. To address this, we introduce protected group networks and propose a novel fairness-modularity metric. This metric extends traditional modularity by explicitly incorporating fairness, and we prove that minimizing it yields naturally fair partitions for protected groups while maintaining theoretical soundness. We develop a general optimization framework for fairness partitioning and design the efficient Fair Fast Newman (FairFN) algorithm, enhancing the Fast Newman (FN) method to optimize both modularity and fairness. Experiments show FairFN achieves significantly improved fairness and high-quality partitions compared to state-of-the-art methods, especially on unbalanced datasets.
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