We did not compare our approach to the GraphSAGE embeddings (Hamilton et al., 2017b) and Graph Convolutional Networks (Schlichtkrull et al., 2018), since they make use of input node features, which are absent in our setup.
additionally, graph embeddings can be of importance in privacy-sensitive network datasets, since in this setup, explicitly storing edges is not required anymore.