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The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

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Published on: January 19, 2019

Interplay between collective behavior and spreading dynamics on complex networks.

Kezan Li1, Zhongjun Ma, Zhen Jia

  • 1School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, People's Republic of China. lkzzr@guet.edu.cn

Chaos (Woodbury, N.Y.)
|January 3, 2013
PubMed
Summary
This summary is machine-generated.

Collective behavior can slow down spreading phenomena in complex networks, while spreading can accelerate collective behavior. Enhancing awareness of collective behavior is key to controlling network spread.

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Area of Science:

  • Complex networks
  • Network science
  • Spreading dynamics

Background:

  • Correlations exist between collective behavior and spreading dynamics in complex networks.
  • Traditional physical models and dynamical characteristics inform network behavior analysis.

Purpose of the Study:

  • To construct novel bidirectional network models for spreading phenomena.
  • To analyze the interplay between collective and spreading behaviors in these models.

Main Methods:

  • Theoretical and numerical analysis of newly constructed bidirectional network models.
  • Lyapunov function method to determine the spread threshold in spreading networks.

Main Results:

  • Collective behavior inhibits spreading behavior.
  • Spreading behavior accelerates collective behavior.
  • Enhancing individual awareness of collective behavior is an effective control method.

Conclusions:

  • The study provides a framework for understanding and controlling complex network systems with both collective and spreading dynamics.
  • Findings suggest that manipulating awareness of collective behavior can manage network spread.