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Optimal percolation on multiplex networks.

Saeed Osat1,2, Ali Faqeeh3, Filippo Radicchi4

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Finding the minimal set of nodes to break apart networks, known as optimal percolation, is crucial for disease control and influence spread. This study reveals that applying single-layer network solutions to multiplex networks can misrepresent system robustness.

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Area of Science:

  • Network Science
  • Complex Systems
  • Statistical Physics

Background:

  • Optimal percolation identifies critical node sets for network fragmentation, vital for disease immunization and influence maximization.
  • Existing research primarily focuses on optimal percolation within isolated, single-layer networks.
  • The generalization of optimal percolation to multiplex networks remains an underexplored area.

Purpose of the Study:

  • To investigate the challenges and consequences of applying single-layer network solutions to optimal percolation problems in multiplex networks.
  • To extend existing optimal percolation methods to the multiplex network context.
  • To systematically analyze the robustness of multiplex networks under optimal percolation strategies.

Main Methods:

  • Extension of single-layer optimal percolation algorithms to multiplex network structures.
  • Systematic analysis of synthetic and real-world multiplex network datasets.
  • Comparative evaluation of solution accuracy between single-layer approximations and multiplex-aware methods.

Main Results:

  • Approximating multiplex network solutions using single-layer network methods can lead to significant underestimations of system robustness.
  • The interconnectedness of layers in multiplex networks fundamentally alters percolation dynamics.
  • Developed and tested extended methods for optimal percolation on multiplex networks.

Conclusions:

  • Relying on single-layer network approximations for optimal percolation in multiplex systems can be misleading and detrimental to understanding network resilience.
  • New methodologies are required to accurately assess and manage optimal percolation in complex, multilayered systems.
  • This research highlights the critical need for multiplex-aware strategies in network analysis and intervention.