The assessment of fundamental properties for economic and financial systems, such as systemic risk, is systematically hindered by privacy issues-that put severe limitations on the available information . Here we introduce a novel method to reconstruct  partially-accessible networked systemsof this kind. The method is based on the knowledge of the fitnesses, i.e., intrinsic node-specific properties , and of the number of connections of only a limited subset of nodes. Such information is used to calibrate a directed configuration model which can generate ensembles of  networks intended to represent the real system, so that the real network properties can be estimated within the generated ensemblein terms of mean values of the observables . Here we focus on estimating those properties that are commonly used to measure the network resilience to shock and crashes. Tests on both artificial and empirical networks shows that the method is remarkably robust with respect to the limitedness of the information available, thus representing a valuable tool for gaining insights on privacy-protected economic and financial systems.
We address a fundamental problem that is systematically encountered when modeling complex systems: 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 with their average values within the ensemble . Here 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.
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The assessment of fundamental properties for
We address a fundamental problem that is systematically encountered when modeling complex systems: the limitedness of the information available. In the case of
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systems, such as systemic risk, is systematically hindered by privacy issues-that put severe limitations on the available information
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test the method both on synthetic and empirical networks, focusing on the
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the network resilience to shock and crashes. Tests on both artificial and empirical networks shows that the method is remarkably robust
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