  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.
