Optimizing the robustness of higher-low order coupled networks.

Area of Science:

• Network Science
• Complex Systems
• Systems Engineering

Background:

• Robustness of complex networks is crucial for infrastructure stability.
• Traditional research often overlooks higher-order and coupled network structures.
• Network robustness is typically measured by the largest connected component after attacks.

Purpose of the Study:

• To propose and evaluate robust optimization methods for higher-low order coupled networks.
• To enhance the resilience of interconnected infrastructure systems.
• To address the limitations of existing research on higher-order network robustness.

Main Methods:

• Proposed robust optimization using greedy and simulated annealing algorithms.
• Optimized network topology by randomly reconnecting edges in both higher-order and low-order networks.
• Evaluated robustness using the largest connected component metric on real-world networks.

Main Results:

• Simulated annealing algorithm demonstrated superior performance compared to the greedy algorithm.
• The proposed optimization methods effectively improved the robustness coefficient of coupled networks.
• Both higher-order and low-order network components showed enhanced resilience.

Conclusions:

• The developed methods significantly enhance the robustness of higher-low order coupled networks.
• Network topology optimization is a viable strategy for improving system resilience.
• Further research can explore these methods for diverse complex systems.