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Modeling information diffusion in time-varying community networks.

Xuelian Cui1, Narisa Zhao1

  • 1Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China.

Chaos (Woodbury, N.Y.)
|January 1, 2018
PubMed
Summary

Social network community structures change over time. Higher mobility and attractiveness boost information spread, especially with strong modularity, but only if communities differ in attractiveness.

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Area of Science:

  • Network Science
  • Computational Social Science
  • Epidemiology

Background:

  • Social networks are dynamic with changing topologies.
  • Existing models often overlook time-varying community structures in social contagion.
  • Understanding how community structure changes impacts information diffusion is crucial.

Purpose of the Study:

  • To investigate the influence of time-varying community structures on information dissemination.
  • To model information diffusion in temporal networks with dynamic modularity.

Main Methods:

  • Developed a continuous-time Markov model for information diffusion.
  • Introduced parameters for mobility rate and community attractiveness to capture time-varying community structure.
  • Derived the basic reproduction number and validated the model using simulation and theoretical results.

Main Results:

  • Both mobility rate and community attractiveness generally enhance information diffusion, particularly during early stages.
  • The promotion effect is stronger with higher modularity.
  • Social mobility does not accelerate diffusion when community attractiveness is uniform.
  • Local spreading in advantageous groups increases due to agglomeration effects from mobility and attractiveness differences, enhancing global spread.

Conclusions:

  • Time-varying community structures significantly impact information diffusion dynamics.
  • Mobility and attractiveness are key drivers of spread, with their effects modulated by network modularity and attractiveness heterogeneity.
  • The findings have implications for understanding and managing information cascades in evolving social networks.