Incorporating First-Order Partial Differential Equations into Graph Neural Networks for Enhanced Performance and Interpretability

This paper presents new Graph Neural Network models that incorporate two first-order Partial Differential Equations (PDEs) - the advection equation and the Burgers equation. These models effectively mitigate the over-smoothing problem in GNNs while maintaining comparable performance to higher-order PDE models.