Graph Neural Networks (GNNs) have demonstrated remarkable success in modeling
complex relationships in graph-structured data. A recent innovation in this
field is the family of Differential Equation-Inspired Graph Neural Networks
(DE-GNNs), which leverage principles from continuous dynamical systems to model
information flow on graphs with built-in properties such as feature smoothing
or preservation. However, existing DE-GNNs rely on first or second-order
temporal dependencies. In this paper, we propose a neural extension to those
pre-defined temporal dependencies. We show that our model, called TDE-GNN, can
capture a wide range of temporal dynamics that go beyond typical first or
second-order methods, and provide use cases where existing temporal models are
challenged. We demonstrate the benefit of learning the temporal dependencies
using our method rather than using pre-defined temporal dynamics on several
graph benchmarks.

本文提出了一种名为 TDE-GNN 的模型，它可以捕捉超过典型的一阶或二阶方法的各种时间动力学，并提供了现有时间模型难以处理的用例。通过在几个图形基准上学习时间依赖性，我们证明了使用我们的方法学习时间依赖性而不是使用预定义时间动态的好处。