Please use this identifier to cite or link to this item:
Title: Latent geometry of bipartite networks
Authors: Kitsak, Maksim A. 
Papadopoulos, Fragkiskos 
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

Show full item record

Citations 10

checked on Oct 18, 2019

Citations 20

checked on Oct 22, 2019

Page view(s) 20

Last Week
Last month
checked on Oct 23, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.