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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.
Nature Communications
|February 26, 2026
Summary
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.
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.
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