通过双教师知识蒸来实现公平的图形神经网络
Chengyu Li1, Debo Cheng2, Guixian Zhang3
1School of Computer Science and Engineering, Guangxi Key Lab of MSMI; MOE Key Lab of EBIT, Guangxi Normal University, Guilin, 541004, Guangxi, China.
概括
本研究介绍了FairDTD,这是一种用于图形神经网络 (GNN) 的新方法,可以减少敏感属性的偏差. 公平DTD通过使用双教师蒸来更好地进行代表性学习,平衡公平性和模型实用性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形表示学习学习学习图形表示学习
背景情况:
- 图形神经网络 (GNN) 在表示学习方面表现出色,但由于敏感的属性,它们经常表现出偏差.
- 现有的GNN公平性方法可以通过使用不完整的数据 (仅限于节点特征或图形结构) 来损害模型效用.
研究的目的:
- 开发一个新的框架,FairDTD,有效平衡GNN的公平性和实用性.
- 解决在偏见的 GNN 预测中公平性和模型性能之间的权衡问题.
主要方法:
- FairDTD使用因果图模型来指导双教师蒸用于公平表示学习.
- 它采用两种以公平为导向的教师模型 (特征和结构教师) 进行双蒸.
- 图表级蒸和节点特定的温度模块增强了信息传输和公平知识传播.
主要成果:
- 公平DTD在各种基准数据集中实现了最佳的公平性.
- 提出的方法有效地保持了高模型效用,同时减轻了偏差.
- 实验证明了FairDTD在GNN公平代表性学习中的卓越性能.
结论:
- 公平DTD提供了一种有效的策略,以实现GNN的公平性和实用性.
- 双教师蒸方法与因果指导是FairDTD成功的关键.
- 该框架推进了对图形数据的公平表示学习领域.
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