  The ever-increasing pace of scientific publication necessitates methods for
quickly identifying relevant papers. While neural recommenders trained on user
interests can help, they still result in long, monotonous lists of suggested
papers. To improve the discovery experience we introduce multiple new methods
for \em augmenting recommendations with textual relevance messages that
highlight knowledge-graph connections between recommended papers and a user's
publication and interaction history. We explore associations mediated by author
entities and those using citations alone. In a large-scale, real-world study,
we show how our approach significantly increases engagement -- and future
engagement when mediated by authors -- without introducing bias towards
highly-cited authors. To expand message coverage for users with less
publication or interaction history, we develop a novel method that highlights
connections with proxy authors of interest to users and evaluate it in a
controlled lab study. Finally, we synthesize design implications for future
graph-based messages.
