  Link prediction aims to reveal missing edges in a graph. We address this task
with a Gaussian process that is transformed using simplified graph convolutions
to better leverage the inductive bias of the domain. To scale the Gaussian
process model to large graphs, we introduce a variational inducing point method
that places pseudo inputs on a graph-structured domain. We evaluate our model
on eight large graphs with up to thousands of nodes and report consistent
improvements over existing Gaussian process models as well as competitive
performance when compared to state-of-the-art graph neural network approaches.
