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|Title:||Latent geometry of bipartite networks||Authors:||Kitsak, Maksim A.
Krioukov, Dmitri V.
|Keywords:||Bipartite systems;Networked systems||Category:||Electrical Engineering - Electronic Engineering - Information Engineering||Field:||Engineering and Technology||Issue Date:||Mar-2017||Publisher:||American Physical Society||Source:||Physical Review E, 2017, vol. 95, no. 3, pp. 032309-1 - 032309-20||Journal:||Physical Review E||Abstract:||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.||ISSN:||1539-3755
|DOI:||10.1103/PhysRevE.95.032309||Rights:||© 2017 American Physical Society.||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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