  Training of Relational Graph Convolutional Networks (R-GCN) is a memory
intense task. The amount of gradient information that needs to be stored during
training for real-world graphs is often too large for the amount of memory
available on most GPUs. In this work, we experiment with the use of graph
summarization techniques to compress the graph and hence reduce the amount of
memory needed. After training the R-GCN on the graph summary, we transfer the
weights back to the original graph and attempt to perform inference on it. We
obtain reasonable results on the AIFB, MUTAG and AM datasets. Our experiments
show that training on the graph summary can yield a comparable or higher
accuracy to training on the original graphs.Furthermore, if we take the time to
compute the summary out of the equation, we observe that the smaller graph
representations obtained with graph summarization methods reduces the
computational overhead. However, further experiments are needed to evaluate
additional graph summary models and whether our findings also holds true for
very large graphs.
