用时空感知的图神经微分方程，处理不规则采样与部分观测数据的系统演化建模。
Modelling Networked Dynamical System by Temporal Graph Neural ODE with Irregularly Partial Observed Time-series Data
- 引入带可靠性评估的时间感知图神经微分方程，融合空间与时间依赖性。
- 在多种网络动态系统上实现高精度状态重建与未来预测，优于传统RNN方法。
- 适合处理不规则采样、部分缺失的复杂系统数据，如传感器网络或生物系统。
在多个领域中，基于时间序列数据建模系统演化是一项关键且具有挑战性的任务，尤其当数据为不规则采样且部分可观测时。现有方法如神经微分方程（Neural ODE）或指数衰减动态函数结合循环神经网络（RNN），虽可估计间隔内隐藏动态，但难以捕捉图结构时间序列中的时空依赖关系，也未能充分利用关联信息进行缺失数据补全与未来状态预测。此外，传统RNN方法使用共享单元更新隐状态，忽略了不同时间间隔及缺失状态对估计可靠性的影响。为此，本文提出一种嵌入可靠性评估与时间感知机制的图神经微分方程方法，可有效建模不规则采样与部分可观测时间序列中的时空依赖关系以重构动态演化。同时设计了考虑增强数据可靠性的损失函数，进一步提升预测性能。所提方法在多种网络化动态系统实验中得到验证。
原文摘要 · Abstract (English)
Modeling the evolution of system with time-series data is a challenging and critical task in a wide range of fields, especially when the time-series data is regularly sampled and partially observable. Some methods have been proposed to estimate the hidden dynamics between intervals like Neural ODE or Exponential decay dynamic function and combine with RNN to estimate the evolution. However, it is difficult for these methods to capture the spatial and temporal dependencies existing within graph-structured time-series data and take full advantage of the available relational information to impute missing data and predict the future states. Besides, traditional RNN-based methods leverage shared RNN cell to update the hidden state which does not capture the impact of various intervals and missing state information on the reliability of estimating the hidden state. To solve this problem, in this paper, we propose a method embedding Graph Neural ODE with reliability and time-aware mechanism which can capture the spatial and temporal dependencies in irregularly sampled and partially observable time-series data to reconstruct the dynamics. Also, a loss function is designed considering the reliability of the augment data from the above proposed method to make further prediction. The proposed method has been validated in experiments of different networked dynamical systems.
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