  We investigate Relational Graph Attention Networks, a class of models that
extends non-relational graph attention mechanisms to incorporate relational
information, opening up these methods to a wider variety of problems. A
thorough evaluation of these models is performed, and comparisons are made
against established benchmarks. To provide a meaningful comparison, we retrain
Relational Graph Convolutional Networks, the spectral counterpart of Relational
Graph Attention Networks, and evaluate them under the same conditions. We find
that Relational Graph Attention Networks perform worse than anticipated,
although some configurations are marginally beneficial for modelling molecular
properties. We provide insights as to why this may be, and suggest both
modifications to evaluation strategies, as well as directions to investigate
for future work.
