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Information evolution in complex networks.

Yang Tian1, Guoqi Li2, Pei Sun1

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Chaos (Woodbury, N.Y.)
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Summary
This summary is machine-generated.

Information evolution in complex networks shows local randomness and global regularity. Our study formalizes this, revealing how network selectivity preserves information against distortion, explaining brain and social network patterns.

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Area of Science:

  • Complex Systems Science
  • Network Theory
  • Information Theory
  • Computational Neuroscience
  • Social Network Analysis

Background:

  • Information evolution in complex networks is crucial for biological and social phenomena.
  • A general theory for information evolution, explaining the paradox of local randomness and global regularity, is lacking.
  • Existing knowledge gaps hinder understanding of information diffusion mechanisms in networks.

Purpose of the Study:

  • To formalize the theory of information evolution in complex networks.
  • To explain the coexistence of local randomness and global regularity in information diffusion.
  • To identify the fundamental laws governing information survival and dissipation.

Main Methods:

  • Application of network dynamics principles.
  • Utilization of information theory frameworks.
  • Analysis of information selectivity and diversity within networks.

Main Results:

  • A significant portion of information survives random distortion due to network selectivity.
  • Information dissipation rates are determined by the diversity of network selectivity.
  • Discovered laws hold irrespective of noise, though noise can disrupt them.

Conclusions:

  • The study provides a formal framework for understanding information evolution in complex networks.
  • Network selectivity is key to preserving information against local randomness.
  • The findings are validated across diverse systems, including neural tuning and social opinion dynamics.