dc.contributor.author | Saracco, F | |
dc.contributor.author | Di Clemente, R | |
dc.contributor.author | Gabrielli, A | |
dc.contributor.author | Squartini, T | |
dc.date.accessioned | 2020-01-29T09:40:46Z | |
dc.date.issued | 2015-06-01 | |
dc.description.abstract | 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. | en_GB |
dc.description.sponsorship | GROWTHCOM | en_GB |
dc.identifier.citation | Vol. 5 | en_GB |
dc.identifier.doi | 10.1038/srep10595 | |
dc.identifier.grantnumber | 611272 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/40624 | |
dc.language.iso | en | en_GB |
dc.publisher | Nature Research | en_GB |
dc.rights | This work is licensed under a Creative Commons Attribution 4.0 International License. The
images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the
Creative Commons license, users will need to obtain permission from the license holder to reproduce
the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ | en_GB |
dc.title | Randomizing bipartite networks: The case of the World Trade Web | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-01-29T09:40:46Z | |
dc.description | This is the final version. Available from Nature Research via the DOI in this record. | en_GB |
dc.identifier.journal | Scientific Reports | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2015-04-20 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2015-06-01 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-01-29T09:37:23Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2020-01-29T09:40:52Z | |
refterms.panel | B | en_GB |