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This study introduces a new model and nonlinear spectral method for core-periphery detection in multilayer networks. The approach identifies core and peripheral structures in both nodes and layers, offering new insights into complex systems.

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Area of Science:

  • Network Science
  • Complex Systems Analysis
  • Data Mining

Background:

  • Multilayer networks model systems with multiple interaction types.
  • Core-periphery detection identifies central (core) and outer (peripheral) network structures.
  • Existing methods often struggle with the complexity of multilayered, weighted, and directed networks.

Purpose of the Study:

  • To propose a novel model for core-periphery structure in multilayer networks.
  • To develop a nonlinear spectral method for simultaneous node and layer core-periphery detection.
  • To analyze structural insights in diverse empirical multilayer networks.

Main Methods:

  • Developed a new mathematical model for multilayer core-periphery structure.
  • Implemented a nonlinear spectral analysis technique.
  • Applied the method to weighted and directed multilayer networks.

Main Results:

  • Successfully detected core and periphery structures in both nodes and layers simultaneously.
  • Revealed novel structural insights in three distinct empirical networks.
  • Demonstrated the method's applicability to citation, transport, and trade networks.

Conclusions:

  • The proposed model and method effectively capture core-periphery organization in complex multilayer networks.
  • The nonlinear spectral approach provides valuable insights into network architecture across different domains.
  • This work advances the understanding of structure in weighted and directed multilayer systems.