eprintid: 2200
rev_number: 8
eprint_status: archive
userid: 6
dir: disk0/00/00/22/00
datestamp: 2014-06-16 11:16:52
lastmod: 2014-07-07 10:28:55
status_changed: 2014-06-16 11:16:52
type: article
metadata_visibility: show
creators_name: Musmeci, Nicolò
creators_name: Battiston, Stefano 
creators_name: Caldarelli, Guido
creators_name: Puliga, Michelangelo
creators_name: Gabrielli, Andrea
creators_id: 
creators_id: 
creators_id: guido.caldarelli@imtlucca.it
creators_id: michelangelo.puliga@imtlucca.it
creators_id: 
title: Bootstrapping topological properties and systemic risk of complex networks using the fitness model
ispublished: pub
subjects: HA
subjects: HG
subjects: QC
divisions: EIC
full_text_status: none
keywords: Complex networks; Financial systems
abstract: In this paper we present a novel method to reconstruct global topological properties of a complex network starting from limited information. We assume to know for all the nodes a non-topological quantity that we interpret as fitness. In contrast, we assume to know the degree, i.e. the number of connections, only for a subset of the nodes in the network. We then use a fitness model, calibrated on the subset of nodes for which degrees are known, in order to generate ensembles of networks. Here, we focus on topological properties that are relevant for processes of contagion and distress propagation in networks, i.e. network density and k-core structure, and we study how well these properties can be estimated as a function of the size of the subset of nodes utilized for the calibration. Finally, we also study how well the resilience to distress propagation in the network can be estimated using our method. We perform a first test on ensembles of synthetic networks generated with the Exponential Random Graph model, which allows to apply common tools from statistical mechanics. We then perform a second test on empirical networks taken from economic and financial contexts. In both cases, we find that a subset as small as 10 % of nodes can be enough to estimate the properties of the network along with its resilience with an error of 5 %.
date: 2013
date_type: published
publication: Journal of Statistical Physics
volume: 151
number: 3-4
publisher: Springer
pagerange: 720-734
id_number: 10.1007/s10955-013-0720-1
refereed: TRUE
issn: 0022-4715
official_url: http://dx.doi.org/10.1007/s10955-013-0720-1
citation:   Musmeci, Nicolò and Battiston, Stefano  and Caldarelli, Guido and Puliga, Michelangelo and Gabrielli, Andrea  Bootstrapping topological properties and systemic risk of complex networks using the fitness model.  Journal of Statistical Physics, 151 (3-4).  pp. 720-734.  ISSN 0022-4715      (2013)