Emerging activity temporal hypergraph: A model for generating realistic time-varying hypergraphs

科学分野:

• Complex Systems Science; Network Science; Data Science

背景:

• 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.

研究 の 目的:

• 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.

主な方法:

• 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.

主要な成果:

• 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.

結論:

• 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.