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Influence networks: Bayesian modeling and diffusion.

Samuel Sánchez-Gutiérrez1, Juan Sosa1, Carolina Luque2

  • 1Departamento de Estadística, Universidad Nacional de Colombia, Bogotá, Colombia.

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Summary
This summary is machine-generated.

This study introduces a new Bayesian model to measure individual influence and simulate idea spread in social networks. The model quantizes influencing capacity and latent social positions, aiding in understanding information diffusion dynamics.

Keywords:
91D30Bayesian modelingRelational datadiffusion of ideasinfluencelatent space modelssocial networks

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Area of Science:

  • Social Network Analysis
  • Computational Social Science
  • Bayesian Modeling

Background:

  • Understanding influence dynamics in social networks is crucial for analyzing information diffusion.
  • Existing models may not fully capture individual influencing capacity or latent social positions.

Purpose of the Study:

  • To develop an innovative Bayesian latent space model for analyzing influence networks.
  • To establish a formal metric for quantifying individual influencing capacity and estimating latent social positions.
  • To introduce a novel mechanism for simulating idea diffusion based on estimated latent characteristics.

Main Methods:

  • Adaptation of a Bayesian latent space model using novel projections.
  • Reparameterization of the model to define metrics for influence and position.
  • Simulation of idea diffusion with individuals in states: Unknown, undecided, supporting, or rejecting.

Main Results:

  • A formal metric for quantifying influencing capacity was established.
  • Individuals' latent positions within a social space were estimated.
  • A novel diffusion simulation mechanism was introduced and evaluated.

Conclusions:

  • The proposed Bayesian model offers a robust framework for analyzing influence networks and simulating idea diffusion.
  • The method provides quantifiable metrics for individual influence and social positioning.
  • The approach was successfully demonstrated on a real-world influence network.