Ktisis Cyprus University of Technologyhttps://ktisis.cut.ac.cyThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Wed, 05 Aug 2020 05:20:33 GMT2020-08-05T05:20:33Z5021- Latent geometry of bipartite networkshttps://ktisis.cut.ac.cy/handle/10488/10049Title: Latent geometry of bipartite networks
Authors: Kitsak, Maksim A.; Papadopoulos, Fragkiskos; Krioukov, Dmitri V.
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.
Wed, 08 Mar 2017 00:00:00 GMThttps://ktisis.cut.ac.cy/handle/10488/100492017-03-08T00:00:00Z
- Hyperbolic geometry of complex networkshttps://ktisis.cut.ac.cy/handle/10488/13900Title: Hyperbolic geometry of complex networks
Authors: Krioukov, Dmitri; Boguñá, Marián; Vahdat, Amin; Papadopoulos, Fragkiskos; Kitsak, Maksim A.
Abstract: We develop a geometric framework to study the structure and function of complex networks. We assume that hyperbolic geometry underlies these networks, and we show that with this assumption, heterogeneous degree distributions and strong clustering in complex networks emerge naturally as simple reflections of the negative curvature and metric property of the underlying hyperbolic geometry. Conversely, we show that if a network has some metric structure, and if the network degree distribution is heterogeneous, then the network has an effective hyperbolic geometry underneath. We then establish a mapping between our geometric framework and statistical mechanics of complex networks. This mapping interprets edges in a network as noninteracting fermions whose energies are hyperbolic distances between nodes, while the auxiliary fields coupled to edges are linear functions of these energies or distances. The geometric network ensemble subsumes the standard configuration model and classical random graphs as two limiting cases with degenerate geometric structures. Finally, we show that targeted transport processes without global topology knowledge, made possible by our geometric framework, are maximally efficient, according to all efficiency measures, in networks with strongest heterogeneity and clustering, and that this efficiency is remarkably robust with respect to even catastrophic disturbances and damages to the network structure. © 2010 The American Physical Society.
Thu, 09 Sep 2010 00:00:00 GMThttps://ktisis.cut.ac.cy/handle/10488/139002010-09-09T00:00:00Z