在社区检测中衡量群体公平性
Elze de Vink1, Frank W Takes1, Akrati Saxena1
1Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands.
PloS one
|November 11, 2025
概括
本研究为社区检测算法引入了新的群体公平性指标,解决了网络中影响少数群体的不平等问题. Infomap和Significance方法在各种网络中显示出强大的性能和公平性.
科学领域:
- 网络分析 网络分析
- 算法的公平性算法公平性
- 社会技术系统 社会技术系统
背景情况:
- 社区结构是网络分析的基础,并由种族和性别等社会因素塑造.
- 现实世界的网络表现出结构上的不平等,有多数和少数群体.
- 传统的社区检测算法可能会为代表性不足的群体产生不公平的结果.
研究的目的:
- 为评估社区检测方法提出新的群体公平度指标.
- 进行对常见社区检测算法的性能和公平性进行比较分析.
- 调查社区检测中的绩效和公平性之间的权衡.
主要方法:
- 开发适用于社区检测的新群体公平度指标.
- 在合成 (LFR,ABCD,HICH-BA) 和现实世界的网络上对社区检测算法的比较评估.
- 在不同的算法方法中分析性能-公平性权衡.
主要成果:
- 公平性-绩效的权衡在社区检测方法之间有很大的差异.
- 没有一种单一的方法可以始终优化性能和公平性.
- Infomap和Significance方法在各种社区类型和网络中显示出高性能和公平性.
结论:
- 现有的社区检测方法表现出各种公平性表现特征.
- 拟议的指标为评估和改进算法公平性提供了一个框架.
- 洞察力引导设计更公平,更有效的社区检测算法,用于现实世界的网络.
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