  Graphs are a common model for complex relational data such as social networks
and protein interactions, and such data can evolve over time (e.g., new
friendships) and be noisy (e.g., unmeasured interactions). Link prediction aims
to predict future edges or infer missing edges in the graph, and has diverse
applications in recommender systems, experimental design, and complex systems.
Even though link prediction algorithms strongly depend on the set of edges in
the graph, existing approaches typically do not modify the graph topology to
improve performance. Here, we demonstrate how simply adding a set of edges,
which we call a \emph{proposal set}, to the graph as a pre-processing step can
improve the performance of several link prediction algorithms. The underlying
idea is that if the edges in the proposal set generally align with the
structure of the graph, link prediction algorithms are further guided towards
predicting the right edges; in other words, adding a proposal set of edges is a
signal-boosting pre-processing step. We show how to use existing link
prediction algorithms to generate effective proposal sets and evaluate this
approach on various synthetic and empirical datasets. We find that proposal
sets meaningfully improve the accuracy of link prediction algorithms based on
both neighborhood heuristics and graph neural networks. Code is available at
\url{https://github.com/CUAI/Edge-Proposal-Sets}.
