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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.

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|December 23, 2025
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Summary
This summary is machine-generated.

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.

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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.