15 citations · 40 across the 5 of their papers we have counts for
5 papers
Understanding convolution on graphs via energies
Francesco Di Giovanni, James Rowbottom, Benjamin P. Chamberlain +2
Graph Neural Networks (GNNs) typically operate by message-passing, where the state of a node is updated based on the information received from its neighbours. Most message-passing…
Equivariant Mesh Attention Networks
Sourya Basu, Jose Gallego-Posada, Francesco Viganò +2
Equivariance to symmetries has proven to be a powerful inductive bias in deep learning research. Recent works on mesh processing have concentrated on various kinds of natural symme…
Graph-Coupled Oscillator Networks
T. Konstantin Rusch, Benjamin P. Chamberlain, James Rowbottom +2
We propose Graph-Coupled Oscillator Networks (GraphCON), a novel framework for deep learning on graphs. It is based on discretizations of a second-order system of ordinary differen…
Beltrami Flow and Neural Diffusion on Graphs
Benjamin Paul Chamberlain, James Rowbottom, Davide Eynard +3
We propose a novel class of graph neural networks based on the discretised Beltrami flow, a non-Euclidean diffusion PDE. In our model, node features are supplemented with positiona…
GRAND: Graph Neural Diffusion
Benjamin Paul Chamberlain, James Rowbottom, Maria Gorinova +3
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…