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Beltrami flow and neural diffusion on graphs

Abstract:
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 positional encodings derived from the graph topology and jointly evolved by the Beltrami flow, producing simultaneously continuous feature learning and topology evolution. The resulting model generalises many popular graph neural networks and achieves state-of-the-art results on several benchmarks.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher:
Curran Associates Publisher's website
Volume:
3
Pages:
1594-1609
Host title:
Advances in Neural Information Processing Systems 34
Publication date:
2022-05-03
Acceptance date:
2021-09-28
Event title:
35th Conference on Neural Information Processing Systems (NeurIPS 2021)
Event location:
Virtual event
Event website:
https://nips.cc/Conferences/2021
Event start date:
2021-12-14T00:00:00Z
Event end date:
2021-12-16T00:00:00Z
ISSN:
1049-5258
ISBN:
9781713845393
Language:
English
Keywords:
Pubs id:
1265689
Local pid:
pubs:1265689
Deposit date:
2022-09-13

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