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Contact-based social contagion in multiplex networks.

Emanuele Cozzo1, Raquel A Baños1, Sandro Meloni2

  • 1Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza 50009, Spain and Department of Theoretical Physics, University of Zaragoza, Zaragoza 50009, Spain.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
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Summary
This summary is machine-generated.

We present a new model for understanding how information spreads through multiplex social networks. Our findings show that the largest eigenvalue of a network layer determines the critical point for information contagion.

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Area of Science:

  • Complex Systems Science
  • Network Science
  • Sociophysics

Background:

  • Social contagion, or the spread of behaviors and information, is a key phenomenon in large-scale social systems.
  • Existing models often simplify social structures, failing to capture the complexity of multiple communication channels.

Purpose of the Study:

  • To develop a theoretical framework for studying epidemic-like social contagion in multiplex networks.
  • To identify the key factors determining the critical point of information spreading in systems with multiple interconnected layers.

Main Methods:

  • Proposed a contact-based information spreading model for multiplex networks.
  • Analyzed the critical point of contagion using eigenvalue analysis of contact probability matrices.
  • Applied the framework to various simulated and real-world multiplex systems.

Main Results:

  • The critical point for information contagion in a multiplex system is determined by the network layer with the largest eigenvalue.
  • Aggregating multiplex layers into a single network provides inaccurate predictions of information dissemination.
  • The developed framework accurately models contagion in diverse multiplex network scenarios.

Conclusions:

  • Multiplex network structure significantly impacts social contagion dynamics.
  • Understanding individual layer properties, particularly eigenvalue, is crucial for predicting epidemic thresholds.
  • Simplified aggregated network models are insufficient for accurately describing information spread in complex, multi-platform social systems.