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A spread willingness computing-based information dissemination model.

Haojing Huang1, Zhiming Cui2, Shukui Zhang3

  • 1Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, China ; Department of Computer Information Engineering, Guangdong Technical College of Water Resource and Electric Engineering, Guangzhou 510635, China.

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|August 12, 2014
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Summary
This summary is machine-generated.

This study introduces a social network information spread model considering node degree and user behavior factors. Findings reveal that weaker relationships and higher contact frequency accelerate information dissemination, even from low-degree nodes.

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Area of Science:

  • Social Network Analysis
  • Information Dissemination Dynamics
  • Complex Network Theory

Background:

  • Understanding information spread in social networks is crucial for analyzing user behavior.
  • Existing models often overlook the nuanced factors influencing user willingness to share information.

Purpose of the Study:

  • To develop a spread willingness computing model for social networks.
  • To analyze the impact of node degree, relationship strength, views identity, and contact frequency on information dissemination.

Main Methods:

  • Constructed an information dissemination model integrating complex network theory and infectious disease dynamics.
  • Established dynamical evolution equations to describe node interactions over time.
  • Incorporated user-specific factors: relationship strength, views identity, and contact frequency.

Main Results:

  • Node degree, regardless of its magnitude, follows similar dissemination trends.
  • Weaker node relationships, higher views identity alignment, and increased contact frequency significantly enhance information spread.
  • Dissemination speed is sensitive to changes in dissemination and immune probabilities.

Conclusions:

  • The proposed model accurately reflects social network characteristics and user behavior.
  • Identified key factors driving rapid and widespread information dissemination.
  • Provides a framework for analyzing and predicting information spread patterns in online social environments.