Attack Vulnerability of Network Controllability.

Area of Science:

• Network Science
• Systems Theory
• Control Theory

Background:

• Controllability of complex networks is crucial for system understanding and management.
• Assessing network robustness against attacks and failures is practically significant.

Purpose of the Study:

• To systematically investigate the attack vulnerability of network controllability.
• To compare the impact of node vs. edge attacks and different centrality-based strategies.
• To evaluate the robustness of model and real-world networks.

Main Methods:

• Simulated node and edge attacks on canonical model networks (Barabási-Albert, Erdős-Rényi) and real-world networks.
• Attack strategies based on degree and betweenness centralities (initial and recalculated).
• Comparison with random failure scenarios.

Main Results:

• Node-based attacks are generally more detrimental to controllability than edge-based attacks.
• Recalculated centrality strategies are more harmful than initial ones.
• Barabási-Albert networks are most vulnerable; Erdős-Rényi networks are most robust.
• Most real-world networks exhibit robustness to random node failures.
• Recalculated betweenness centrality is the most effective attack strategy for real networks.

Conclusions:

• Network controllability robustness varies significantly with network structure and attack strategy.
• Real-world networks demonstrate resilience to random failures, unlike some model networks.
• Edge degree is an unreliable metric for assessing edge importance in controllability.