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Targeting attack activity-driven networks.

Dandan Zhao1, Li Wang1, Bo Zhang2

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Targeted attacks on temporal networks reveal vulnerabilities. Increasing the deletion probability of highly active nodes enhances network robustness against temporal percolation, but extreme heterogeneity decreases it.

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Area of Science:

  • Complex systems science
  • Network science
  • Statistical physics

Background:

  • Real-world systems exhibit dynamic temporal features, necessitating temporal network analysis over static models.
  • Traditional static network analysis fails to capture the evolving topology of complex systems.
  • Understanding network resilience against targeted attacks is crucial for system stability.

Purpose of the Study:

  • To investigate the temporal percolation properties and resilience of activity-driven temporal networks.
  • To propose and analyze an activity-based targeted attack strategy.
  • To evaluate the impact of node activity on network robustness.

Main Methods:

  • Developed an activity-based targeted attack model for temporal networks.
  • Utilized percolation theory and generating functions to analyze the giant component and temporal percolation threshold.
  • Employed a network mapping framework based on node activity.
  • Validated theoretical results with simulation data.

Main Results:

  • Targeted attacks significantly impact temporal networks, unlike random attacks.
  • Increasing the deletion probability of highly active nodes enhances the temporal percolation threshold and giant component, improving robustness.
  • Extremely heterogeneous activity distributions in networks decrease overall robustness.
  • Theoretical predictions align with simulation outcomes near the percolation threshold.

Conclusions:

  • Activity-based targeted attacks are effective in disrupting temporal networks.
  • Node activity is a critical factor in determining temporal network resilience.
  • Network robustness is sensitive to the heterogeneity of node activity distributions.
  • Findings provide insights into the analysis and understanding of real-world temporal networks.