  Despite the abundance of bipartite networked systems, their organizing
principles are less studied, compared to unipartite networks. Bipartite
networks are often analyzed after projecting them onto one of the two sets of
nodes. As a result of the projection, nodes of the same set are linked together
if they have at least one neighbor in common in the bipartite network. Even
though these projections allow one to study bipartite networks using tools
developed for unipartite networks, one-mode projections lead to significant
loss of information and artificial inflation of the projected network with
fully connected subgraphs. Here we pursue a different approach for analyzing
bipartite systems that is based on the observation that such systems have a
latent metric structure: network nodes are points in a latent metric space,
while connections are more likely to form between nodes separated by shorter
distances. This approach has been developed for unipartite networks, and
relatively little is known about its applicability to bipartite systems. Here,
we fully analyze a simple latent-geometric model of bipartite networks, and
show that this model explains the peculiar structural properties of many real
bipartite systems, including the distributions of common neighbors and
bipartite clustering. We also analyze the geometric information loss in
one-mode projections in this model, and propose an efficient method to infer
the latent pairwise distances between nodes. Uncovering the latent geometry
underlying real bipartite networks can find applications in diverse domains,
ranging from constructing efficient recommender systems to understanding cell
metabolism.
