relation: http://eprints.imtlucca.it/2831/
title: Randomizing bipartite networks: the case of the World Trade Web
creator: Saracco, Fabio
creator: Di Clemente, Riccardo
creator: Gabrielli, Andrea
creator: Squartini, Tiziano
subject: HB Economic Theory
subject: QA Mathematics
description: Within the last fifteen years, network theory has been successfully applied both to natural sciences and to socioeconomic disciplines. In particular, bipartite networks have been recognized to provide a particularly insightful representation of many systems, ranging from mutualistic networks in ecology to trade networks in economy, whence the need of a pattern detection-oriented analysis in order to identify statistically-significant structural properties. Such an analysis rests upon the definition of suitable null models, i.e. upon the choice of the portion of network structure to be preserved while randomizing everything else. However, quite surprisingly, little work has been done so far to define null models for real bipartite networks. The aim of the present work is to fill this gap, extending a recently-proposed method to randomize monopartite networks to bipartite networks. While the proposed formalism is perfectly general, we apply our method to the binary, undirected, bipartite representation of the World Trade Web, comparing the observed values of a number of structural quantities of interest with the expected ones, calculated via our randomization procedure. Interestingly, the behavior of the World Trade Web in this new representation is strongly different from the monopartite analogue, showing highly non-trivial patterns of self-organization.
publisher: Nature Publishing Group
date: 2015
type: Article
type: PeerReviewed
format: application/pdf
language: en
rights: cc_by_nc
identifier: http://eprints.imtlucca.it/2831/1/srep10595%20%281%29.pdf
identifier:   Saracco, Fabio and Di Clemente, Riccardo and Gabrielli, Andrea and Squartini, Tiziano  Randomizing bipartite networks: the case of the World Trade Web.  Scientific Reports, 5.  p. 10595.  ISSN 2045-2322      (2015)  
relation: http://www.nature.com/articles/srep10595
relation: 10.1038/srep10595