eprintid: 2629
rev_number: 12
eprint_status: archive
userid: 6
dir: disk0/00/00/26/29
datestamp: 2015-03-09 09:41:23
lastmod: 2018-03-08 16:57:03
status_changed: 2015-03-09 09:41:23
type: book_section
metadata_visibility: show
creators_name: Cimini, Giulio
creators_name: Squartini, Tiziano
creators_name: Musmeci, Nicolò
creators_name: Puliga, Michelangelo
creators_name: Gabrielli, Andrea
creators_name: Garlaschelli, Diego
creators_name: Battiston, Stefano
creators_name: Caldarelli, Guido
creators_id: giulio.cimini@imtlucca.it
creators_id: tiziano.squartini@imtlucca.it
creators_id: 
creators_id: michelangelo.puliga@imtlucca.it
creators_id: 
creators_id: diego.garlaschelli@imtlucca.it
creators_id: 
creators_id: guido.caldarelli@imtlucca.it
title: Reconstructing topological properties of complex networks using the fitness model
ispublished: pub
subjects: H1
subjects: HB
subjects: QC
divisions: EIC
full_text_status: public
keywords: Complex networks; Network reconstruction; Exponential random graphs; Fitness model
note: SocInfo 2014 International Workshops, Barcelona, Spain, November 11, 2014, Revised Selected Papers
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, 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.
date: 2015-02
date_type: published
series: Lecture Notes in Computer Science
number: 8852
publisher: Springer
pagerange: 323-333
id_number: 10.1007/978-3-319-15168-7_41
refereed: TRUE
isbn: 978-3-319-15168-7
book_title: Social Informatics
official_url: http://dx.doi.org/10.1007/978-3-319-15168-7_41
citation:   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)  
document_url: http://eprints.imtlucca.it/2629/1/1410.2121v1.pdf