eprintid: 2832
rev_number: 10
eprint_status: archive
userid: 69
dir: disk0/00/00/28/32
datestamp: 2015-11-05 14:34:43
lastmod: 2018-03-08 16:56:43
status_changed: 2015-11-05 14:34:43
type: article
metadata_visibility: show
creators_name: Cimini, Giulio
creators_name: Squartini, Tiziano
creators_name: Gabrielli, Andrea
creators_name: Garlaschelli, Diego
creators_id: giulio.cimini@imtlucca.it
creators_id: tiziano.squartini@imtlucca.it
creators_id: 
creators_id: diego.garlaschelli@imtlucca.it
title: Estimating topological properties of weighted networks from limited information
ispublished: pub
subjects: QA
subjects: QC
divisions: EIC
full_text_status: public
abstract: A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here, we develop a reconstruction method, based on statistical mechanics concepts, that makes use of the empirical link density in a highly nontrivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems.
date: 2015-10
date_type: published
publication: Physical Review E
volume: 92
publisher: American Physical Society
pagerange: 040802
id_number: 10.1103/PhysRevE.92.040802
refereed: TRUE
issn: 1539-3755
official_url: http://link.aps.org/doi/10.1103/PhysRevE.92.040802
citation:   Cimini, Giulio and Squartini, Tiziano and Gabrielli, Andrea and Garlaschelli, Diego  Estimating topological properties of weighted networks from limited information.  Physical Review E, 92.  040802.  ISSN 1539-3755      (2015)  
document_url: http://eprints.imtlucca.it/2832/1/1409.6193v2.pdf