  The inception of the Relational Graph Convolutional Network (R-GCN) marked a
milestone in the Semantic Web domain as a widely cited method that generalises
end-to-end hierarchical representation learning to Knowledge Graphs (KGs).
R-GCNs generate representations for nodes of interest by repeatedly aggregating
parameterised, relation-specific transformations of their neighbours. However,
in this paper, we argue that the the R-GCN's main contribution lies in this
"message passing" paradigm, rather than the learned weights. To this end, we
introduce the "Random Relational Graph Convolutional Network" (RR-GCN), which
leaves all parameters untrained and thus constructs node embeddings by
aggregating randomly transformed random representations from neighbours, i.e.,
with no learned parameters. We empirically show that RR-GCNs can compete with
fully trained R-GCNs in both node classification and link prediction settings.
