Communicability reveals a transition to coordinated behavior in multiplex networks.

Area of Science:

• Complex systems science
• Network theory
• Information theory

Background:

• Multiplex networks consist of multiple layers of interconnected nodes.
• The communicability function measures information flow efficiency in simple networks.
• Understanding information flow in complex systems is crucial for their performance.

Purpose of the Study:

• To generalize the communicability function for multiplex networks.
• To analyze information flow dynamics in real-world multiplex systems.
• To identify conditions for optimal system performance in multiplex networks.

Main Methods:

• Generalization of the communicability function from simple graphs to multiplex networks.
• Application of the generalized communicability function to a social multiplex network (formal-informal channels).
• Application of the generalized communicability function to an air transportation network (different airlines as layers).

Main Results:

• The communicability function was successfully generalized to multiplex networks.
• Information flow analysis revealed that optimal system performance emerges from coordinated layer operations.
• System performance in multiplex networks differs significantly from unconnected networks.

Conclusions:

• The generalized communicability function provides a valuable tool for analyzing information flow in multiplex networks.
• Coordinated operation across network layers is essential for efficient complex system performance.
• This approach offers insights into the behavior of real-world systems like social networks and transportation systems.