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Emerging activity temporal hypergraph: A model for generating realistic time-varying hypergraphs
Marco Mancastroppa1, Giulia Cencetti1, Alain Barrat1
1CPT, CNRS, Université de Toulon, Aix Marseille Univ, Turing Center for Living Systems, 13009 Marseille, France.
Physical Review. E
|December 23, 2025
Summary
A new model, the Emerging Activity Temporal Hypergraph (EATH), generates synthetic temporal hypergraphs that mimic real-world group interactions. This allows for better understanding of complex systems and dynamical processes, even with limited data.
Area of Science:
- Complex Systems Science
- Network Science
- Data Science
Background:
- Time-varying group interactions are fundamental to complex systems.
- Temporal hypergraphs capture higher-order, time-dependent interactions.
- Empirical datasets are often incomplete, necessitating surrogate models.
Purpose of the Study:
- Introduce a novel temporal hypergraph model (EATH) for generating synthetic datasets.
- Enable the study of dynamical processes on complex interaction networks.
- Facilitate understanding of systems with limited or incomplete interaction data.
Main Methods:
- Developed the Emerging Activity Temporal Hypergraph (EATH) model.
- EATH uses node activity dynamics and memory mechanisms to generate interactions.
- Validated EATH against empirical face-to-face interaction datasets.
Main Results:
- EATH successfully generated surrogate temporal hypergraphs mirroring empirical data properties.
- Simulations of higher-order contagion dynamics showed comparable outcomes on real and synthetic data.
- Demonstrated EATH's flexibility in creating tunable and hybrid hypergraphs.
Conclusions:
- The EATH model provides a powerful tool for creating realistic synthetic temporal hypergraphs.
- Synthetic data aids in studying complex system dynamics where data collection is challenging.
- Opens new avenues for understanding emergent behaviors in group interactions.
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