在分布之外的图表上概括图形神经网络
IEEE transactions on pattern analysis and machine intelligence
|October 2, 2023
概括
图形神经网络 (GNN) 难以应对分布的变化. 稳定GNN使用因果推理来提取强大的子图表示,通过专注于真实相关性来改善分布外设置中的概括.
科学领域:
- 图形神经网络的神经网络
- 因果推理因果推理
- 机器学习 机器学习
背景情况:
- 图形神经网络 (GNN) 在外分发 (OOD) 设置中经常失败,原因是依赖训练数据的虚假相关性.
- 国际调查局 (I.I.D.) 的情况. 标准机器学习中的假设导致GNN利用非因果关系,阻碍了概括.
研究的目的:
- 开发一个稳定的图形神经网络 (GNN) 框架,以减轻在外分发 (OOD) 场景中的性能退化.
- 通过因果表示方法消除虚假的相关性来解决GNN概括的退化.
主要方法:
- 提出 StableGNN,这是GNN的因果表示框架,从因果角度分析退化.
- 使用可微分的图形聚合层进行端到端提取基于子图的表示.
- 引入因果变量区分调整器,以混平衡为灵感,通过样本权重来纠正偏差的训练分布.
主要成果:
- 稳定GNN在合成和现实世界的OOD图表数据集上显著优于最先进的方法.
- 该框架在增强现有GNN架构方面表现出灵活性.
- 可解释性实验证实了StableGNN利用因果结构进行准确预测的能力.
结论:
- 稳定GNN提供了一个强大的解决方案,用于在分布转移下稳定的图形神经网络 (GNN) 性能.
- 因果表示框架有效地消除了虚假的相关性,从而改善了OOD设置中的概括和可靠预测.
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