  In this paper, we describe a reproduction of the Relational Graph
Convolutional Network (RGCN). Using our reproduction, we explain the intuition
behind the model. Our reproduction results empirically validate the correctness
of our implementations using benchmark Knowledge Graph datasets on node
classification and link prediction tasks. Our explanation provides a friendly
understanding of the different components of the RGCN for both users and
researchers extending the RGCN approach. Furthermore, we introduce two new
configurations of the RGCN that are more parameter efficient. The code and
datasets are available at https://github.com/thiviyanT/torch-rgcn.
