Natural Computing

, Volume 13, Issue 3, pp 379–390

A cognitive-inspired algorithm for growing networks

  • Emanuele Massaro
  • Franco Bagnoli
  • Andrea Guazzini
  • Henrik Olsson
Article

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.

Keywords

Complex networks Computational modelling Growing networks 

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Emanuele Massaro
    • 1
    • 2
    • 3
  • Franco Bagnoli
    • 4
    • 5
  • Andrea Guazzini
    • 6
  • Henrik Olsson
    • 7
    • 8
  1. 1.Department of Information Engineering and Center for the Study of Complex SystemsUniversity of FlorenceFlorenceItaly
  2. 2.Risk and Decision Science TeamUS Army Engineer Research Development CenterConcordUSA
  3. 3.Department of Civil and Environmental EngineeringCarnegie Mellon UniversityPittsburghUSA
  4. 4.Department of Physics and Astronomy, Center for the Study of Complex SystemsUniversity of FlorenceFlorenceItaly
  5. 5.INFNFlorenceItaly
  6. 6.Department of Science of Education and Psychology, Center for the Study of Complex SystemsUniversity of FlorenceFlorenceItaly
  7. 7.Center for Adaptive Behavior and CognitionMax Planck Institute for Human DevelopmentBerlinGermany
  8. 8.Department of PsychologyUniversity of WarwickCoventryUK

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