eprintid: 3749
rev_number: 12
eprint_status: archive
userid: 69
dir: disk0/00/00/37/49
datestamp: 2017-08-04 10:38:09
lastmod: 2018-03-08 16:56:04
status_changed: 2017-08-04 10:38:09
type: article
metadata_visibility: show
creators_name: Squartini, Tiziano
creators_name: Cimini, Giulio
creators_name: Gabrielli, Andrea
creators_name: Garlaschelli, Diego
creators_id: tiziano.squartini@imtlucca.it
creators_id: giulio.cimini@imtlucca.it
creators_id: 
creators_id: diego.garlaschelli@imtlucca.it
title: Network reconstruction via density sampling
ispublished: pub
subjects: QA
subjects: QA75
subjects: QC
divisions: EIC
full_text_status: public
abstract: Reconstructing weighted networks from partial information is necessary in many important circumstances, e.g. for a correct estimation of systemic risk. It has been shown that, in order to achieve an accurate reconstruction, it is crucial to reliably replicate the empirical degree sequence, which is however unknown in many realistic situations. More recently, it has been found that the knowledge of the degree sequence can be replaced by the knowledge of the strength sequence, which is typically accessible, complemented by that of the total number of links, thus considerably relaxing the observational requirements. Here we further relax these requirements and devise a procedure valid when even the the total number of links is unavailable. We assume that, apart from the heterogeneity induced by the degree sequence itself, the network is homogeneous, so that its (global) link density can be estimated by sampling subsets of nodes with representative density. We show that the best way of sampling nodes is the random selection scheme, any other procedure being biased towards unrealistically large, or small, link densities. We then introduce our core technique for reconstructing both the topology and the link weights of the unknown network in detail. When tested on real economic and financial data sets, our method achieves a remarkable accuracy and is very robust with respect to the sampled subsets, thus representing a reliable practical tool whenever the available topological information is restricted to small portions of nodes.
date: 2017-01
date_type: published
publication: Applied Network Science
volume: 2
number: 1
publisher: Springer
pagerange: 3
id_number: 10.1007/s41109-017-0021-8
refereed: TRUE
issn: 2364-8228
official_url: https://doi.org/10.1007/s41109-017-0021-8
projects: EU project CoeGSS (grant num. 676547)
projects: EU project Multiplex (grant num. 317532)
projects: EU project Shakermaker (grant num. 687941)
projects: EU project SoBigData (nr. 654024)
projects: EU project GrowthCom (grant num. 611272)
projects: FET project SIMPOL (grant num. 610704)
projects: FET project DOLFINS (grant num. 640772)
citation:   Squartini, Tiziano and Cimini, Giulio and Gabrielli, Andrea and Garlaschelli, Diego  Network reconstruction via density sampling.  Applied Network Science, 2 (1).  p. 3.  ISSN 2364-8228      (2017)  
document_url: http://eprints.imtlucca.it/3749/1/10.1007%252Fs41109-017-0021-8.pdf