2015 | OriginalPaper | Buchkapitel
Reconstructing Topological Properties of Complex Networks Using the Fitness Model
verfasst von : Giulio Cimini, Tiziano Squartini, Nicolò Musmeci, Michelangelo Puliga, Andrea Gabrielli, Diego Garlaschelli, Stefano Battiston, Guido Caldarelli
Erschienen in: Social Informatics
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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.