通过图表增强来促进链接预测中的公平性
Yezi Liu1, Hanning Chen2, Mohsen Imani2
1Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States.
Frontiers in big data
|November 8, 2024
概括
公平链接通过创建公平性增强图表来增强网络分析,提高链接预测准确性和敏感群体之间的公平性. 这种可扩展的方法适用于现实世界的应用.
科学领域:
- 网络分析 网络分析
- 机器学习是机器学习.
- 算法的公平性算法公平性
背景情况:
- 链接预测至关重要,但容易产生偏见,特别是在敏感属性方面.
- 目前在图形嵌入中使用的调解方法使大型网络上的训练变得复杂.
研究的目的:
- 为公平链接预测开发一种新的方法,在预测器培训过程中绕过复杂的 debiasing.
- 确保链接预测独立于敏感节点属性.
主要方法:
- 提出FairLink,一种学习公平性增强图形的方法.
- 通过反映原图的训练轨迹来保持准确性.
- 通过尽量减少敏感群体之间的概率差异来提高公平性.
主要成果:
- 公平链接促进公平,同时保持竞争链接预测准确性.
- 增强的图表显示了在各种图形神经网络 (GNN) 架构中强大的通用性.
- 对于大规模的现实世界图表部署,FairLink具有高度可扩展性.
结论:
- 公平链接为公平链接预测提供了一个可扩展和有效的解决方案.
- 该方法成功地平衡了网络分析中的公平性和准确性.
- 公平链接的通用性使其能够适应各种GNN模型.
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