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Synchronous and asynchronous recursive random scale-free nets.

Francesc Comellas1, Hernan D Rozenfeld, Daniel ben-Avraham

  • 1Departament de Matemàtica Aplicada IV, EPSC, Universitat Politècnica de Catalunya, Avinguda del Canal Olímpic s/n, 08860 Castelldefels, Barcelona, Catalonia, Spain. comellas@mat.upc.es

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 31, 2005
PubMed
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We found that updating rules in scale-free recursive networks significantly impact their structure. Random sequential updates, unlike deterministic ones, can introduce bias, affecting network properties like the degree exponent.

Area of Science:

  • Network Science
  • Complex Systems
  • Statistical Physics

Background:

  • Scale-free recursive networks are models for complex systems with applications in various fields.
  • Previous studies have observed discrepancies in network properties, such as the degree exponent, between different construction methods.

Purpose of the Study:

  • To investigate the differences between scale-free recursive networks generated by synchronous, deterministic versus asynchronous, random sequential updating rules.
  • To identify the underlying cause of observed discrepancies in the degree exponent between these network types.

Main Methods:

  • Comparison of network properties (degree exponent) between Apollonian nets (synchronous, deterministic) and random Apollonian nets (asynchronous, random sequential).
  • Analysis of the updating process in asynchronous random sequential networks to identify potential biases.

Related Experiment Videos

Main Results:

  • Significant discrepancies in the degree exponent were observed between deterministic and random sequential updating rules.
  • The study demonstrates that a biased selection of units during sequential updating in random Apollonian nets is responsible for these discrepancies.

Conclusions:

  • The choice of updating rule critically influences the structural properties of scale-free recursive networks.
  • Careful consideration of updating mechanisms is essential for accurate modeling and analysis of complex networks.