relation: http://eprints.imtlucca.it/2629/
title: Reconstructing topological properties of complex networks using the fitness model
creator: Cimini, Giulio
creator: Squartini, Tiziano
creator: Musmeci, Nicolò
creator: Puliga, Michelangelo
creator: Gabrielli, Andrea
creator: Garlaschelli, Diego
creator: Battiston, Stefano
creator: Caldarelli, Guido
subject: H Social Sciences (General)
subject: HB Economic Theory
subject: QC Physics
description: A 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.
publisher: Springer
date: 2015-02
type: Book Section
type: PeerReviewed
format: application/pdf
language: en
rights: cc_by_nc
identifier: http://eprints.imtlucca.it/2629/1/1410.2121v1.pdf
identifier:   Cimini, Giulio and Squartini, Tiziano and Musmeci, Nicolò and Puliga, Michelangelo and Gabrielli, Andrea and Garlaschelli, Diego and Battiston, Stefano and Caldarelli, Guido  Reconstructing topological properties of complex networks using the fitness model.   In:  Social Informatics.   Lecture Notes in Computer Science  (8852).  Springer, pp. 323-333.  ISBN 978-3-319-15168-7      (2015)  
relation: http://dx.doi.org/10.1007/978-3-319-15168-7_41
relation: 10.1007/978-3-319-15168-7_41