  At most 1-2% of the global virome has been sampled to date. Recent work has
shown that predicting which host-virus interactions are possible but
undiscovered or unrealized is, fundamentally, a network science problem. Here,
we develop a novel method that combines a coarse recommender system (Linear
Filtering; LF) with an imputation algorithm based on low-rank graph embedding
(Singular Value Decomposition; SVD) to infer host-virus associations. This
combination of techniques results in informed initial guesses based on directly
measurable network properties (density, degree distribution) that are refined
through SVD (which is able to leverage emerging features). Using this method,
we recovered highly plausible undiscovered interactions with a strong signal of
viral coevolutionary history, and revealed a global hotspot of unusually unique
but unsampled (or unrealized) host-virus interactions in the Amazon rainforest.
We develop several tests for quantifying the bias and realism of these
predictions, and show that the LF-SVD method is robust in each aspect. We
finally show that graph embedding of the imputed network can be used to improve
predictions of human infection from viral genome features, showing that the
global structure of the mammal-virus network provides additional insights into
human disease emergence.
