  Adoption of recently developed methods from machine learning has given rise
to creation of drug-discovery knowledge graphs (KG) that utilize the
interconnected nature of the domain. Graph-based modelling of the data,
combined with KG embedding (KGE) methods, are promising as they provide a more
intuitive representation and are suitable for inference tasks such as
predicting missing links. One common application is to produce ranked lists of
genes for a given disease, where the rank is based on the perceived likelihood
of association between the gene and the disease. It is thus critical that these
predictions are not only pertinent but also biologically meaningful. However,
KGs can be biased either directly due to the underlying data sources that are
integrated or due to modeling choices in the construction of the graph, one
consequence of which is that certain entities can get topologically
overrepresented. We demonstrate the effect of these inherent structural
imbalances, resulting in densely-connected entities being highly ranked no
matter the context. We provide support for this observation across different
datasets, models as well as predictive tasks. Further, we present various graph
perturbation experiments which yield more support to the observation that KGE
models can be more influenced by the frequency of entities rather than any
biological information encoded within the relations. Our results highlight the
importance of data modeling choices, and emphasizes the need for practitioners
to be mindful of these issues when interpreting model outputs and during KG
composition.
