Before revision: In order to overcome the expressive limitations of graph neural networks (GNNs) , we propose the first method that exploits vector flows over graphs to develop globally consistent directional and asymmetric aggregation functions.
After revision: In order to overcome the expressive limitations of graph neural networks (GNNs) strongly limits their expressiveness, contributing to well-known issues such as over-smoothing. To overcome this limitation, we propose the first method that exploits vector flows over graphs to develop globally consistent directional and asymmetric aggregation functions.