Related papers: Graph Anisotropic Diffusion

Graph Anisotropic Diffusion

URL: http://arxiv.org/abs/2205.00354v1
Date: Sat, 30 Apr 2022 22:13:20 GMT
Title: Graph Anisotropic Diffusion
Authors: Ahmed A. A. Elhag, Gabriele Corso, Hannes St\"ark, Michael M. Bronstein
Abstract summary: We present a new GNN architecture called Graph Anisotropic Diffusion. Our model alternates between linear diffusion, for which a closed-form solution is available, and local anisotropic filters to obtain efficient multi-hop anisotropic kernels.
Score: 15.22604744349679
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Traditional Graph Neural Networks (GNNs) rely on message passing, which amounts to permutation-invariant local aggregation of neighbour features. Such a process is isotropic and there is no notion of `direction' on the graph. We present a new GNN architecture called Graph Anisotropic Diffusion. Our model alternates between linear diffusion, for which a closed-form solution is available, and local anisotropic filters to obtain efficient multi-hop anisotropic kernels. We test our model on two common molecular property prediction benchmarks (ZINC and QM9) and show its competitive performance.

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