Characterization of interactions' persistence in time-varying networks
Francisco Bauzá Mingueza1,2, Mario Floría2,3, Jesús Gómez-Gardeñes2,3
1Department of Theoretical Physics, University of Zaragoza, 50006, Zaragoza, Spain.
Scientific Reports
|January 14, 2023
Summary
We introduce temporality to measure network persistence. Empirical networks show higher persistence than random networks, revealing insights into dynamic system interactions.
Area of Science:
- Network science
- Complex systems dynamics
- Data analysis
Background:
- Complex networked systems have dynamic interactions that impact processes.
- Understanding the persistence of these interactions is crucial.
Purpose of the Study:
- To introduce a new descriptor, temporality, for quantifying network interaction persistence.
- To analyze the specialness of time-varying networks within their configuration space.
- To investigate the influence of temporal resolution on network persistence.
Main Methods:
- Development and application of the temporality descriptor.
- Calculation of average temporality ([Formula: see text]) for network sequences.
- Comparison of empirical network temporality with randomized counterparts.
- Analysis of temporal resolution effects on temporality.
Main Results:
- Empirical network interaction sequences are significantly more similar (persistent) than randomized ones.
- The average temporality ([Formula: see text]) quantifies network specialness.
- Temporal resolution impacts the measured temporality of network interactions.
Conclusions:
- Temporality effectively quantifies the persistence of interactions in time-varying networks.
- Real-world networks exhibit higher interaction persistence than random models.
- The study highlights the importance of temporal resolution in network analysis.
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