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How memory generates heterogeneous dynamics in temporal networks.

Christian L Vestergaard1, Mathieu Génois1, Alain Barrat2

  • 1Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, 13288 Marseille, France.

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Summary
This summary is machine-generated.

Temporal network dynamics show significant heterogeneity. This study reveals that memory effects in link creation and deletion drive these dynamics, impacting processes like epidemic spreading.

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Area of Science:

  • Network Science
  • Complex Systems
  • Data Science

Background:

  • Empirical temporal networks exhibit significant dynamic heterogeneities.
  • These heterogeneities profoundly influence processes like rumor and epidemic spreading.
  • Understanding the microscopic origins of these temporal network dynamics is limited.

Purpose of the Study:

  • To investigate how microscopic mechanisms at the node and link level lead to heterogeneous dynamics in temporal networks.
  • To model temporal networks incorporating memory effects in link dynamics.
  • To analyze the impact of these mechanisms on network heterogeneity and spreading processes.

Main Methods:

  • Developed a generative modeling framework for temporal networks.
  • Incorporated long-term memory effects in link creation and disappearance mechanisms.
  • Employed analytical and numerical methods to study emergent network properties and dynamical processes.

Main Results:

  • Identified long-term memory effects in empirical temporal network link dynamics.
  • Demonstrated the emergence of heterogeneous distributions of contact durations, intercontact durations, and contacts per link.
  • Quantified the individual effects of these heterogeneities on epidemic spreading models.

Conclusions:

  • Memory effects in link dynamics are a key driver of heterogeneity in temporal networks.
  • Distributions of intercontact durations and contacts per link critically influence spreading dynamics.
  • The generative model provides insights into the emergence of complex temporal network behaviors.