A paper titled Composition-based Multi-relational Graph Convolutional Networks (Vashishth et al., ICLR 2020). The basic idea is that most of the GNN-based methods that are popular are used on undirected and simple graph structures, whereas multi-relational and directed graphs are actually what's important in the real world. CompGCN (the model proposed in the paper) embeds both the nodes and relations in knowledge graphs in order to incorporate this information. The "composition" comes in order to effectively and jointly learn the embeddings for entities and relations. Previous models for multi-relational graphs are limited to only learning entities, due to computational complexity reasons.