  Background: Network propagation has been widely used for nearly 20 years to
predict gene functions and phenotypes. Despite the popularity of this approach,
little attention has been paid to the question of provenance tracing in this
context, e.g., determining how much any experimental observation in the input
contributes to the score of every prediction.
  Results: We design a network propagation framework with two novel components
and apply it to predict human proteins that directly or indirectly interact
with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to
its experimentally validated sources, which in our case are human proteins
experimentally determined to interact with viral proteins. Second, we design a
technique that helps to reduce the manual adjustment of parameters by users. We
find that for every top-ranking prediction, the highest contribution to its
score arises from a direct neighbor in a human protein-protein interaction
network. We further analyze these results to develop functional insights on
SARS-CoV-2 that expand on known biology such as the connection between
endoplasmic reticulum stress, HSPA5, and anti-clotting agents.
  Conclusions: We examine how our provenance tracing method can be generalized
to a broad class of network-based algorithms. We provide a useful resource for
the SARS-CoV-2 community that implicates many previously undocumented proteins
with putative functional relationships to viral infection. This resource
includes potential drugs that can be opportunistically repositioned to target
these proteins. We also discuss how our overall framework can be extended to
other, newly-emerging viruses.
