GRAND: Graph Neural Diffusion

Understand

We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE.

• In our model, the layer structure and topology correspond to the discretisation choices of temporal and spatial operators.
• Our approach allows a principled development of a broad new class of GNNs that are able to address the common plights of graph learning models such as depth, oversmoothing, and bottlenecks.
• Key to the success of our models are stability with respect to perturbations in the data and this is addressed for both implicit and explicit discretisation schemes.