  The ability of a graph neural network (GNN) to leverage both the graph
topology and graph labels is fundamental to building discriminative node and
graph embeddings. Building on previous work, we theoretically show that edGNN,
our model for directed labeled graphs, is as powerful as the Weisfeiler-Lehman
algorithm for graph isomorphism. Our experiments support our theoretical
findings, confirming that graph neural networks can be used effectively for
inference problems on directed graphs with both node and edge labels. Code
available at https://github.com/guillaumejaume/edGNN.
