IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2022-08-14T20:58:25ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2016-04-07T09:17:11Z2016-04-07T09:17:11Zhttp://eprints.imtlucca.it/id/eprint/3392This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/33922016-04-07T09:17:11ZBootstrapping Topological Properties and Systemic Risk of Complex Networks Using the Fitness ModelIn 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, andNicolò MusmeciStefano BattistonGuido Caldarelliguido.caldarelli@imtlucca.itMichelangelo Puligamichelangelo.puliga@imtlucca.itAndrea Gabrielli2015-03-09T09:41:23Z2018-03-08T16:57:03Zhttp://eprints.imtlucca.it/id/eprint/2629This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/26292015-03-09T09:41:23ZReconstructing topological properties of complex networks using the fitness modelA major problem in the study of complex socioeconomic systems is represented by privacy issues—that can put severe limitations on the amount of accessible information, forcing to build models on the basis of incomplete knowledge. In this paper we investigate a novel method to reconstruct global topological properties of a complex network starting from limited information. This method uses the knowledge of an intrinsic property of the nodes (indicated as fitness), and the number of connections of only a limited subset of nodes, in order to generate an ensemble of exponential random graphs that are representative of the real systems and that can be used to estimate its topological properties. Here we focus in particular on reconstructing the most basic properties that are commonly used to describe a network: density of links, assortativity, clustering. We test the method on both benchmark synthetic networks and real economic and financial systems, finding a remarkable robustness with respect to the number of nodes used for calibration. The method thus represents a valuable tool for gaining insights on privacy-protected systems.Giulio Ciminigiulio.cimini@imtlucca.itTiziano Squartinitiziano.squartini@imtlucca.itNicolò MusmeciMichelangelo Puligamichelangelo.puliga@imtlucca.itAndrea GabrielliDiego Garlaschellidiego.garlaschelli@imtlucca.itStefano BattistonGuido Caldarelliguido.caldarelli@imtlucca.it2014-06-16T11:16:52Z2014-07-07T10:28:55Zhttp://eprints.imtlucca.it/id/eprint/2200This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/22002014-06-16T11:16:52ZBootstrapping topological properties and systemic risk of complex networks using the fitness modelIn 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 %.Nicolò MusmeciStefano BattistonGuido Caldarelliguido.caldarelli@imtlucca.itMichelangelo Puligamichelangelo.puliga@imtlucca.itAndrea Gabrielli