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Graph neural networks (GNNs) have evolved into one of the most popular deeplearning architectures. However, GNNs suffer from over-smoothing nodeinformation and, therefore, struggle to solve tasks where global graphproperties are relevant. We introduce G-Signatures, a novel graph learningmethod that enables global graph propagation via randomized signatures.G-Signatures use a new graph conversion concept to embed graph structuredinformation which can be interpreted as paths in latent space. We furtherintroduce the idea of latent space path mapping. This allows us to iterativelytraverse latent space paths, and, thus globally process information.G-Signatures excel at extracting and processing global graph properties, andeffectively scale to large graph problems. Empirically, we confirm theadvantages of G-Signatures at several classification and regression tasks.

G-Signatures: Global Graph Propagation With Randomized Signatures