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GRAND: Graph Neural Diffusion
Benjamin Chamberlain · James Rowbottom · Maria Gorinova · Stefan Webb · Emanuele Rossi · Michael Bronstein

Tue Dec 14 06:45 AM -- 07:30 AM (PST) @
in The Symbiosis of Deep Learning and Differential Equations »
Event URL: https://openreview.net/forum?id=_1fu_cjsaRE »

We present Graph Neural Diffusion (GRAND), a model 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. We develop linear and nonlinear versions of GRAND, achieving competitive results on many standard graph benchmarks.

Author Information

Benjamin Chamberlain (Twitter)
James Rowbottom (Twitter)
Maria Gorinova (University of Edinburgh)
Stefan Webb (Oxford)
Emanuele Rossi (Twitter)
Michael Bronstein (Imperial College London / Twitter)

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