  Prokaryotic viruses, which infect bacteria and archaea, are key players in
microbial communities. Predicting the hosts of prokaryotic viruses helps
decipher the dynamic relationship between microbes. Experimental methods for
host prediction cannot keep pace with the fast accumulation of sequenced
phages. Thus, there is a need for computational host prediction. Despite some
promising results, computational host prediction remains a challenge because of
the limited known interactions and the sheer amount of sequenced phages by
high-throughput sequencing technologies. The state-of-the-art methods can only
achieve 43\% accuracy at the species level. In this work, we formulate host
prediction as link prediction in a knowledge graph that integrates multiple
protein and DNA-based sequence features. Our implementation named CHERRY can be
applied to predict hosts for newly discovered viruses and to identify viruses
infecting targeted bacteria. We demonstrated the utility of CHERRY for both
applications and compared its performance with 11 popular host prediction
methods. To our best knowledge, CHERRY has the highest accuracy in identifying
virus-prokaryote interactions. It outperforms all the existing methods at the
species level with an accuracy increase of 37\%. In addition, CHERRY's
performance on short contigs is more stable than other tools.
