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We introduce REDS, a new model extending random geometric graphs (RGGs) to include link-sharing costs in spatial networks. REDS and its rewiring methods offer valuable tools for studying social and epidemiological dynamics in complex networks.

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Area of Science:

  • Complex Systems
  • Network Science
  • Epidemiology

Background:

  • Relational network models often ignore spatial distances, yet distances significantly impact link formation and maintenance in real-world social networks.
  • Random geometric graphs (RGGs) are common synthetic models for social networks, but they do not fully capture real-world link-sharing dynamics.

Purpose of the Study:

  • To propose REDS, a novel model extending RGGs to incorporate the cost-sharing effects of neighboring links in spatial networks.
  • To analyze the statistical properties and epidemiological behavior of networks generated by REDS using different rewiring strategies.

Main Methods:

  • Developed the REDS model, an extension of energy-constrained RGGs, to account for synergistic link-cost sharing among neighbors.
  • Applied two rewiring procedures: the standard Watts-Strogatz method and a novel method preserving degree distribution to mitigate spatial correlation issues.
  • Analyzed network properties (clustering, assortativity) and epidemiological dynamics using a susceptible-infected-susceptible (SIS) model.

Main Results:

  • Significant differences were observed in statistical properties and epidemiological behavior between RGGs and REDS.
  • The two rewiring schemes within the REDS model also produced distinct network characteristics and disease spread patterns.
  • REDS networks demonstrated considerable differences compared to RGGs, particularly as rewiring increased and infection parameters were altered.

Conclusions:

  • REDS, especially with the implemented rewiring mechanisms, provides a more realistic and insightful framework for modeling social and epidemiological phenomena in synthetic complex networks.
  • The study highlights the importance of incorporating spatial and cost-sharing factors into network models for accurate simulation of real-world processes.