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Area of Science:

  • Network Science
  • Statistical Physics
  • Complex Systems

Background:

  • Multiplex networks, composed of multiple interconnected layers, are prevalent in real-world systems.
  • Understanding their resilience to random failures (site-percolation) is crucial for system stability.
  • Existing methods for analyzing percolation in multiplex networks have limitations in accuracy.

Purpose of the Study:

  • To introduce an exact mathematical framework for describing site-percolation transitions in real multiplex networks.
  • To validate the framework's accuracy using diverse real-world network data.
  • To assess the robustness of multiplex networks against random node removal.

Main Methods:

  • Development of an exact mathematical framework based on the locally treelike ansatz.
  • Analysis of the average percolation diagram over infinite random configurations.
  • Application and testing of the framework on social, biological, and transportation multiplex graphs.

Main Results:

  • The proposed framework accurately predicts percolation diagrams in various multiplex networks.
  • The method demonstrates improved prediction accuracy compared to existing approaches.
  • Results confirm the inherent robustness of real multiplex networks.

Conclusions:

  • The developed mathematical framework provides a reliable tool for analyzing percolation in multiplex networks.
  • Multiplex networks exhibit robust connectivity, with no abrupt changes during random component destruction.
  • The findings have implications for understanding and designing resilient complex systems.