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Growing networks with communities: A distributive link model.

Ke-Ke Shang1, Bin Yang1, Jack Murdoch Moore2

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This summary is machine-generated.

This study introduces a new network growth model that naturally creates modular structures and community evolution. It explains how newer nodes and communities can gain dominance over time, unlike traditional popularity-based models.

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Area of Science:

  • Complex systems
  • Network science
  • Computational biology

Background:

  • The Barabasi-Albert model explains scale-free networks using evolution and popularity.
  • Understanding network evolution is key to explaining the ubiquity of complex and scale-free networks.

Purpose of the Study:

  • To propose a novel, simple network growth model based on the evolution principle.
  • To generate modular networks with evolving communities and analyze their dynamics.
  • To provide a unified explanation for regular and tree-like network communities.

Main Methods:

  • Adopting the evolution principle for network growth.
  • Introducing a new model with a single free parameter to control community dynamics.
  • Analyzing the emergence and dominance of communities over time.

Main Results:

  • The model naturally evolves modular networks with multiple, dynamically sized communities.
  • Under certain conditions, the model generates tree-like networks with distinct community structures.
  • New communities can mitigate the degree growth of hub nodes by absorbing link resources.
  • The model demonstrates a "tyranny of the newcomer" where newer entities achieve dominance.

Conclusions:

  • The proposed model offers a unified explanation for community structures in both regular and tree-like networks.
  • It challenges the "early adopter" principle by showing how new nodes and communities can rise to prominence.
  • The framework is applicable to real-world evolutionary networks, such as the SARS-CoV-2 haplotype network.