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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.