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Higher-order correlations reveal complex memory in temporal hypergraphs
Luca Gallo1, Lucas Lacasa2, Vito Latora3,4,5,6
1Department of Network and Data Science, Central European University, Vienna, Austria. gallol@ceu.edu.
Nature Communications
|June 4, 2024
Summary
This study introduces time-varying hypergraphs to analyze group dynamics in complex systems. It reveals memory in non-Markovian group interactions, crucial for understanding social system patterns.
Area of Science:
- Complex systems science
- Network science
- Sociophysics
Background:
- Real-world systems involve dynamic group interactions.
- Existing temporal network methods focus only on pairwise interactions, missing group dynamics.
Purpose of the Study:
- To develop a framework for analyzing temporal group dynamics using time-varying hypergraphs.
- To characterize the temporal organization of complex systems based on higher-order correlations.
Main Methods:
- Utilized time-varying hypergraphs to model group interactions.
- Introduced a framework based on higher-order correlations for temporal organization analysis.
- Developed a model of temporal hypergraphs with non-Markovian group interactions.
Main Results:
- Identified coherent and interdependent mesoscopic structures in human interaction data.
- Captured aggregation, fragmentation, and nucleation processes in social systems.
- Revealed complex memory as a key mechanism in emerging temporal patterns.
Conclusions:
- Time-varying hypergraphs and higher-order correlations provide a powerful approach to study group dynamics.
- Non-Markovian group interactions and memory are fundamental to the temporal organization of complex social systems.
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