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