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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, clusterin

@article{ title = {Reconstructing Topological Properties of Complex Networks Using the Fitness Model}, type = {article}, year = {2015}, identifiers = {[object Object]}, pages = {323-333}, volume = {8852}, websites = {http://link.springer.com/chapter/10.1007/978-3-319-15168-7_41}, id = {9c887488-f092-3bcb-8aa0-ef7a7234c0f0}, created = {2015-11-26T06:58:17.000Z}, file_attached = {false}, profile_id = {6240f337-b876-3458-ba67-f900377913bd}, group_id = {30cd8884-140e-38cb-b8ff-2fa26ae56359}, last_modified = {2017-04-07T12:26:13.919Z}, read = {false}, starred = {false}, authored = {false}, confirmed = {true}, hidden = {false}, citation_key = {cimini2015reconstructing}, source_type = {article}, folder_uuids = {7c80d829-bb80-46f1-a40e-c34b4ac5cd05}, private_publication = {false}, abstract = {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, clusterin}, bibtype = {article}, author = {Cimini, Giulio and Squartini, Tiziano and Musmeci, Nicolò and Puliga, Michelangelo and Gabrielli, Andrea and Garlaschelli, Diego and Battiston, Stefano and Caldarelli, Guido}, journal = {Lecture Notes in Computer Science} }

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