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学习动态图嵌入与神经控制微分方程的神经控制微分方程
IEEE transactions on pattern analysis and machine intelligence
|October 3, 2025
概括
我们介绍了图形神经控制微分方程 (GN-CDEs),这是动态图形表示学习的新框架. 这种方法有效地建模了复杂的时间相互作用和不断变化的图形结构.
科学领域:
- 机器学习 机器学习
- 图形神经网络 图形神经网络
- 动态系统 动态系统
背景情况:
- 动态图表由于结合节点和结构动态存在挑战.
- 现有的方法与时间图演变的复杂性作斗争.
- 连续时间模型为更准确的动态图表表示提供了潜力.
研究的目的:
- 为动态图表表示学习开发一个统一的连续时间框架.
- 为了共同建模节点嵌入和图形结构动态.
- 为了解决由图形中的时间相互作用引起的复杂性.
主要方法:
- 拟议的图形神经控制微分方程 (GN-CDEs) 框架.
- 整合了一个图形增强的神经网络向量场作为控制信号.
- 利用时间变化的图形路径来表示不断演变的图形结构.
主要成果:
- 证明了在没有零碎集成的情况下在不断演变的图表上建模动态的能力.
- 展示了轨迹校准与后续数据的能力.
- 在动态图数据中表现出对缺失观测的强度.
结论:
- 在演变的图表中,GN-CDEs有效地捕捉了复杂的动态.
- 连续时间框架为动态图表表示学习提供了优势.
- 拟议的方法对各种动态图任务具有前景.
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