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Network rewiring dynamics with convergence towards a star network.

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  • 1Information Science Department , University of Otago , Dunedin, New Zealand.

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|November 16, 2016
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Summary
This summary is machine-generated.

A new rewiring algorithm (RtoS) creates diverse network structures, from regular to star-like. This method enhances network analysis for applications like disease spread and genetic models.

Keywords:
graphpower-lawscale-freesmall-worldstar network

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Area of Science:

  • Network science
  • Computational modeling
  • Complex systems

Background:

  • Network rewiring methods were introduced by Watts & Strogatz in 1998, enabling transitions from regular to random networks.
  • Subsequent research focused on rewiring approaches to generate scale-free networks.
  • Existing methods have limitations in producing a full spectrum of network topologies.

Purpose of the Study:

  • To present a novel rewiring algorithm (RtoS) for generating a continuous range of network structures.
  • To demonstrate the algorithm's capability to transition between regular, small-world, scale-free, and star-like networks.
  • To validate the algorithm's utility through applications in disease spread and genetics models.

Main Methods:

  • Developed the Rewiring to Scale-free (RtoS) algorithm for undirected, non-degenerate, fixed-size networks.
  • Applied the RtoS algorithm to generate networks with varying structural properties.
  • Utilized agent-based models for infectious disease spread and genetic fixation time to test network efficacy.

Main Results:

  • The RtoS algorithm successfully generated networks transitioning through regular, small-world, scale-free, and star-like configurations.
  • Demonstrated improved efficiency in modeling disease spread dynamics on generated networks.
  • Showcased the algorithm's effectiveness in analyzing fixation times in genetic models.

Conclusions:

  • The RtoS algorithm provides a versatile tool for network structure generation.
  • This approach offers a unified framework for studying network evolution and dynamics.
  • The algorithm has broad applicability in modeling complex systems across various scientific domains.