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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Trend-driven information cascades on random networks.

Teruyoshi Kobayashi1

  • 1Graduate School of Economics, Kobe University, 2-1 Rokkodai, Nada, Kobe 657-8501, Japan.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|January 15, 2016
PubMed
Summary
This summary is machine-generated.

Introducing global nodes into threshold models reveals a dual role in collective behavior. These trend followers can accelerate cascades but also reduce the likelihood of trends emerging, with moderate shares potentially maximizing cascade sizes.

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Area of Science:

  • Complex Systems
  • Network Science
  • Sociophysics

Background:

  • Threshold models are widely used for collective behavior, like technology adoption and fads.
  • Standard models focus on local influence, but real-world networks also involve global trends.
  • Individual behavior influences trends, and trends, in turn, influence individual behavior.

Purpose of the Study:

  • To generalize standard threshold models by incorporating global nodes (trend followers).
  • To analyze the complex interplay between local influence and global trends in cascade dynamics.
  • To investigate how global nodes affect cascade emergence and propagation.

Main Methods:

  • Generalization of the standard threshold model.
  • Introduction of 'global nodes' whose activation depends on the global trend (percentage of activated nodes).
  • Analysis of the impact of global nodes on cascade probability and size.

Main Results:

  • Global nodes accelerate cascades once a trend emerges.
  • Global nodes decrease the probability of a trend initially emerging.
  • A moderate proportion of global nodes may maximize the average cascade size.

Conclusions:

  • Global nodes exhibit a dual role, potentially facilitating or inhibiting cascades.
  • The proportion of trend followers is critical in determining cascade dynamics.
  • Optimizing the share of global nodes could be key to managing collective behavior.