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A cognitive-inspired algorithm for growing networks

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Abstract

We present models for generating different classes of networks by adopting simple local strategies and an original model of the evolutionary dynamics and growth of on-line social networks. The model emulates people’s strategies for acquiring information in social networks, emphasising the local subjective view of an individual and what kind of information the individual can acquire when arriving in a new social context. We assume that the strategy proceeds through two phases: (a) a discovery phase, in which the individual becomes aware of the surrounding world and (b) an elaboration phase, in which the individual elaborates locally the information trough a cognitive-inspired algorithm. Model generated networks reproduce the main features of both theoretical and real-world networks, such as high clustering coefficient, low characteristic path length, strong division in communities, and variability of degree distributions.

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Author information

Author notes

  1. Emanuele Massaro

    Present address: Risk and Decision Science Team, US Army Engineer Research Development Center, 696 Virginia Rd, Concord, MA, USA

  2. Henrik Olsson

    Present address: Department of Psychology, University of Warwick, Coventry, CV4 7AL, UK

Authors and Affiliations

  1. Department of Information Engineering and Center for the Study of Complex Systems, University of Florence, Florence, Italy

    Emanuele Massaro

  2. Department of Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA

    Emanuele Massaro

  3. Department of Physics and Astronomy, Center for the Study of Complex Systems, University of Florence, Florence, Italy

    Franco Bagnoli

  4. INFN, Florence, Italy

    Franco Bagnoli

  5. Department of Science of Education and Psychology, Center for the Study of Complex Systems, University of Florence, Florence, Italy

    Andrea Guazzini

  6. Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany

    Henrik Olsson

Authors
  1. Emanuele Massaro
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  2. Franco Bagnoli
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  3. Andrea Guazzini
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  4. Henrik Olsson
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Corresponding author

Correspondence to Emanuele Massaro.

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Massaro, E., Bagnoli, F., Guazzini, A. et al. A cognitive-inspired algorithm for growing networks. Nat Comput 13, 379–390 (2014). https://doi.org/10.1007/s11047-014-9444-7

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  • DOI: https://doi.org/10.1007/s11047-014-9444-7

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