  In this paper, we proposed the \textit{link injection}, a novel method that
helps any differentiable graph machine learning models to go beyond observed
connections from the input data in an end-to-end learning fashion. It finds out
(weak) connections in favor of the current task that is not present in the
input data via a parametric link injection layer. We evaluate our method on
both node classification and link prediction tasks using a series of
state-of-the-art graph convolution networks. Results show that the link
injection helps a variety of models to achieve better performances on both
applications. Further empirical analysis shows a great potential of this method
in efficiently exploiting unseen connections from the injected links.
