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Reconstructing topological properties of complex networks using the fitness model

Author

Listed:
  • Giulio Cimini
  • Tiziano Squartini
  • Nicol`o Musmeci
  • Michelangelo Puliga
  • Andrea Gabrielli
  • Diego Garlaschelli
  • Stefano Battiston
  • Guido Caldarelli

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.

Suggested Citation

  • Giulio Cimini & Tiziano Squartini & Nicol`o Musmeci & Michelangelo Puliga & Andrea Gabrielli & Diego Garlaschelli & Stefano Battiston & Guido Caldarelli, 2014. "Reconstructing topological properties of complex networks using the fitness model," Papers 1410.2121, arXiv.org.
    • Handle: RePEc:arx:papers:1410.2121
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    References listed on IDEAS

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    Cited by:

    1. Piero Mazzarisi & Paolo Barucca & Fabrizio Lillo & Daniele Tantari, 2017. "A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market," Papers 1801.00185, arXiv.org.
    2. Wang, Hu & Li, Shouwei, 2020. "Risk contagion in multilayer network of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    3. Domenico Di Gangi & Giacomo Bormetti & Fabrizio Lillo, 2022. "Score Driven Generalized Fitness Model for Sparse and Weighted Temporal Networks," Papers 2202.09854, arXiv.org, revised Mar 2022.

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