进化图形学习用于非静止环境中的分布外通用化
IEEE transactions on pattern analysis and machine intelligence
|November 11, 2025
概括
本研究介绍了EvoGOOD,这是一个新的框架,可以在面临分布变化的动态图上增强图形神经网络概括. 它通过识别环境意识不变模式来实现优异的分布外预测.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 动态图表对于建模不断演变的系统至关重要.
- 现有的图形神经网络 (GNN) 由于在非静止环境中的分布转移而与分布外 (OOD) 泛化作斗争.
- 了解环境演变是强大的动态图表学习的关键.
研究的目的:
- 提出一个新的框架,用于OOD概括的进化图形学习 (EvoGOOD),以应对动态图形中的OOD概括挑战.
- 研究不断演变的潜伏非静态环境对动态图表生成和GNN性能的影响.
- 在动态图形场景中开发环境意识的不变模式识别方法.
主要方法:
- 设计了一个环境顺序变异自动编码器来模拟环境演变和推断分布.
- 引入了针对环境多样化的环境意识不变模式识别.
- 在节点上应用细粒度的因果干预,使用实例化环境样本进行OOD预测.
主要成果:
- 在分布转移下,EvoGOOD在真实世界和合成动态数据集上展示了卓越的性能.
- 该框架有效地区分了时空不变模式,用于在非静止环境中改进OOD预测.
- 成功模拟环境演变及其对动态图表概括的影响.
结论:
- EvoGOOD为动态图表学习提供了OOD概括的重大进展.
- 这项工作是第一个从环境进化角度研究动态图OOD概括的研究.
- 拟议的方法提高了GNN在动态,非静止环境中的稳定性和适用性.
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