  Dynamic multi-relational graphs are an expressive relational representation
for data enclosing entities and relations of different types, and where
relationships are allowed to vary in time. Addressing predictive tasks over
such data requires the ability to find structure embeddings that capture the
diversity of the relationships involved, as well as their dynamic evolution. In
this work, we establish a novel class of challenging tasks for dynamic
multi-relational graphs involving out-of-domain link prediction, where the
relationship being predicted is not available in the input graph. We then
introduce a novel Graph Neural Network model, named GOOD, designed specifically
to tackle the out-of-domain generalization problem. GOOD introduces a novel
design concept for multi-relation embedding aggregation, based on the idea that
good representations are such when it is possible to disentangle the mixing
proportions of the different relational embeddings that have produced it. We
also propose five benchmarks based on two retail domains, where we show that
GOOD can effectively generalize predictions out of known relationship types and
achieve state-of-the-art results. Most importantly, we provide insights into
problems where out-of-domain prediction might be preferred to an in-domain
formulation, that is, where the relationship to be predicted has very few
positive examples.
