TY  - JOUR
AV  - public
Y1  - 2015///
SN  - 2045-2322
ID  - eprints2834
N2  - We address a fundamental problem that is systematically encountered when modeling real-world complex systems of societal relevance: the limitedness of the information available. In the case of economic and financial networks, privacy issues severely limit the information that can be accessed and, as a consequence, the possibility of correctly estimating the resilience of these systems to events such as financial shocks, crises and cascade failures. Here we present an innovative method to reconstruct the structure of such partially-accessible systems, based on the knowledge of intrinsic node-specific properties and of the number of connections of only a limited subset of nodes. This information is used to calibrate an inference procedure based on fundamental concepts derived from statistical physics, which allows to generate ensembles of directed weighted networks intended to represent the real system?so that the real network properties can be estimated as their average values within the ensemble. We test the method both on synthetic and empirical networks, focusing on the properties that are commonly used to measure systemic risk. Indeed, the method shows a remarkable robustness with respect to the limitedness of the information available, thus representing a valuable tool for gaining insights on privacy-protected economic and financial systems.
JF  - Scientific Reports
TI  - Systemic Risk Analysis on Reconstructed Economic and Financial Networks
PB  - Nature Publishing Group
A1  - Cimini, Giulio
A1  - Squartini, Tiziano
A1  - Garlaschelli, Diego
A1  - Gabrielli, Andrea
VL  - 5
UR  - http://www.nature.com/articles/srep15758
ER  -