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Information dissemination evolution under group feedback.

Y Yang1, F Z Nian1, J S Liu1

  • 1School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China.

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Summary
This summary is machine-generated.

This study explores information dissemination in group networks using a feedback model. Group positivity, not initial size, significantly impacts network evolution in small-world and scale-free networks.

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Area of Science:

  • Network Science
  • Information Dissemination
  • Computational Social Science

Background:

  • Understanding information spread in networked groups is crucial.
  • Existing models often overlook group dynamics and feedback mechanisms.
  • Group behavior significantly influences information diffusion patterns.

Purpose of the Study:

  • To investigate the evolutionary characteristics of group networks during information dissemination.
  • To develop and validate a feedback-based model for information spread in group networks.
  • To analyze the impact of group attributes on network evolution.

Main Methods:

  • Dividing networks into groups with dynamic activity and information response.
  • Developing a feedback model accounting for intra-group and inter-group information flow.
  • Simulating the model on scale-free and small-world networks.
  • Validating the model on a real-world network.

Main Results:

  • Group positivity emerged as the primary driver of evolutionary outcomes in small-world networks, irrespective of initial group size.
  • In scale-free networks, group positivity was the key factor influencing average group degree, independent of initial average degree.
  • The model demonstrated effectiveness and rationality when applied to a real-world network.

Conclusions:

  • Feedback mechanisms and group positivity are critical determinants of information dissemination and network evolution.
  • Network structure (scale-free vs. small-world) interacts with group dynamics to shape information spread.
  • The developed model provides a robust framework for studying information diffusion in complex group networks.