Modelling Networked Dynamical System by Temporal Graph Neural ODE with Irregularly Partial Observed Time-series Data

TL;DR

This paper introduces a novel Temporal Graph Neural ODE model with reliability and time-awareness to effectively capture spatial-temporal dependencies in irregularly sampled, partially observed networked dynamical systems for improved data reconstruction and prediction.

Contribution

It proposes a new graph neural ODE framework with reliability and time-aware mechanisms tailored for irregular, partial time-series data, enhancing dynamic modeling accuracy.

Findings

Effective in reconstructing dynamics of networked systems

Outperforms existing methods in handling irregular sampling

Improves prediction accuracy with reliability-aware loss function

Abstract

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.