TY  - CHAP
Y1  - 2015/02//
T3  - Lecture Notes in Computer Science
SP  - 323
PB  - Springer
A1  - Cimini, Giulio
A1  - Squartini, Tiziano
A1  - Musmeci, Nicoḷ
A1  - Puliga, Michelangelo
A1  - Gabrielli, Andrea
A1  - Garlaschelli, Diego
A1  - Battiston, Stefano
A1  - Caldarelli, Guido
EP  - 333
T2  - Social Informatics
N1  - SocInfo 2014 International Workshops, Barcelona, Spain, November 11, 2014, Revised Selected Papers
ID  - eprints2629
UR  - http://dx.doi.org/10.1007/978-3-319-15168-7_41
KW  - Complex networks; Network reconstruction; Exponential random graphs; Fitness model
TI  - Reconstructing topological properties of complex networks using the fitness model
AV  - public
N2  - 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.
SN  - 978-3-319-15168-7
ER  -