  We explore link prediction as a proxy for automatically surfacing documents
from existing literature that might be topically or contextually relevant to a
new document. Our model uses transformer-based graph embeddings to encode the
meaning of each document, presented as a node within a citation network. We
show that the semantic representations that our model generates can outperform
other content-based methods in recommendation and ranking tasks. This provides
a holistic approach to exploring citation graphs in domains where it is
critical that these documents properly cite each other, so as to minimize the
possibility of any inconsistencies
