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

Network embedding unveils the hidden interactions in the mammalian virome