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Knowledge diffusion of dynamical network in terms of interaction frequency.

Jian-Guo Liu1, Qing Zhou2, Qiang Guo2

  • 1Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics, Shanghai, 200433, P.R. China. liujg004@ustc.edu.cn.

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Summary

This study introduces a social knowledge diffusion (SKD) model where agents share knowledge based on interaction frequency, leading to faster information spread and assortative network structures compared to traditional models.

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Area of Science:

  • Complex systems
  • Network science
  • Information diffusion

Background:

  • Understanding knowledge diffusion in dynamic networks is crucial for social science and information technology.
  • Existing models often overlook the role of interaction frequency in social closeness and knowledge exchange.
  • Dynamic networks evolve through agent interactions and structural changes like edge rewiring.

Purpose of the Study:

  • To introduce and evaluate a novel Social Knowledge Diffusion (SKD) model for dynamic networks.
  • To investigate the impact of interaction frequency versus knowledge distance on diffusion speed and network structure.
  • To explore the coevolution of knowledge diffusion patterns and network topology.

Main Methods:

  • Development of a dynamic network model with agents exchanging knowledge and relocating via edge rewiring.
  • Implementation of a knowledge transfer rule prioritizing interaction frequency over knowledge distance for agent connections.
  • Comparative simulations against a Null model (random selection) and a Traditional Knowledge Diffusion (TKD) model (knowledge distance-based).

Main Results:

  • The SKD model demonstrates significantly faster knowledge spread compared to Null and TKD models.
  • Knowledge diffusion in the SKD model is driven by interaction frequency, reflecting social closeness.
  • The network structure under SKD evolves towards an assortative pattern, characteristic of social networks.

Conclusions:

  • Interaction frequency is a key driver for efficient knowledge diffusion in dynamic social networks.
  • The proposed SKD model accurately captures the coevolution of diffusion dynamics and network structure.
  • Findings offer insights into understanding information spread and social network formation.