Percolation of interdependent networks with limited knowledge.

Area of Science:

• Network Science
• Complex Systems
• Statistical Physics

Background:

• Real-world networks are interconnected, with interdependence being crucial for system robustness.
• Understanding network resilience under various attack scenarios is vital for complex systems.

Purpose of the Study:

• To develop a theoretical framework for analyzing the robustness of interdependent networks under attacks with limited knowledge.
• To investigate the impact of attacker's knowledge level on network fragility.

Main Methods:

• Development of percolation models for single-layer, two-layer, and mixed site-bond percolation with attacks.
• Analysis of network robustness transitions based on attacker's knowledge level.

Main Results:

• Identification of a critical knowledge level that signifies a transition in network robustness.
• Demonstration that random failures across two layers can be more damaging than targeted attacks on a single layer.
• Finding that balanced attack knowledge distribution across layers is most destructive when total knowledge is conserved.

Conclusions:

• Interdependent networks exhibit extreme fragility, particularly under limited attacker knowledge.
• The attacker's knowledge level critically influences network robustness, highlighting a trade-off between connectivity and interdependence.
• Targeted attacks with limited knowledge and random failures on interdependent networks require careful consideration for robust system design.