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Rumor propagation on hypergraphs.

Kleber Andrade Oliveira1, Pietro Traversa2,3, Guilherme Ferraz de Arruda4

  • 1Social Dynamics Research Lab, Department of Psychology, University of Limerick, Limerick, Ireland.

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|February 26, 2026
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Summary
This summary is machine-generated.

This study introduces a new hypergraph model for rumor propagation, accounting for group interactions. The model reveals phase transitions in rumor dynamics, suggesting real-world spread occurs near criticality.

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Area of Science:

  • Complex Systems
  • Information Science
  • Network Science

Background:

  • Social media facilitates rapid information and rumor spread, particularly in group settings.
  • Existing pairwise models fail to capture complex group interactions crucial for rumor dynamics.
  • Higher-order interactions are essential for a comprehensive understanding of information cascades.

Purpose of the Study:

  • To develop a sophisticated higher-order rumor propagation model using hypergraphs.
  • To incorporate a group-based annihilation mechanism into rumor dynamics.
  • To investigate the phase transitions and behaviors of rumor spread in complex networks.

Main Methods:

  • Proposed a novel rumor propagation model based on hypergraphs.
  • Introduced a group-based annihilation mechanism where spreaders become stiflers.
  • Analyzed subcritical dynamics, including exponential and power-law decay, and phase transitions.
  • Validated the model using empirical data from Telegram and email cascades.

Main Results:

  • Identified two distinct subcritical behaviors: exponential and power-law decay.
  • Observed continuous phase transitions in both homogeneous and heterogeneous hypergraphs.
  • Demonstrated coexistence of decay behaviors dependent on hypergraph heterogeneity.
  • Empirical validation confirmed the model's ability to explain real-world rumor dynamics.

Conclusions:

  • The proposed hypergraph model offers a more realistic representation of rumor propagation in group settings.
  • Real-world rumor dynamics frequently operate near a critical state, as suggested by observed phase transitions.
  • The findings provide insights into the mechanisms driving information cascades and their control.