Performance of attack strategies on modular networks

TL;DR

This paper investigates the effectiveness of module-based attack strategies on complex networks, introducing new robustness measures and demonstrating their superiority over traditional attacks in networks with strong community structures.

Contribution

It introduces a generalized robustness measure and an attack performance index, providing a comprehensive analysis of module-based attack strategies on modular networks.

Findings

Non-adaptive module-based attacks outperform adaptive attacks on highly modular networks.

The proposed measures effectively evaluate network robustness and attack efficiency.

Networks with high modularity are more vulnerable to module-based attacks.

Abstract

Vulnerabilities of complex networks have became a trend topic in complex systems recently due to its real world applications. Most real networks tend to be very fragile to high betweenness adaptive attacks. However, recent contributions have shown the importance of interconnected nodes in the integrity of networks and module-based attacks have appeared promising when compared to traditional malicious non-adaptive attacks. In the present work we deeply explore the trade-off associated with attack procedures, introducing a generalized robustness measure and presenting an attack performance index that takes into account both robustness of the network against the attack and the run-time needed to obtained the list of targeted nodes for the attack. Besides, we introduce the concept of deactivation point aimed to mark the point at which the network stops to function properly. We then show empirically that non-adaptive module-based attacks perform better than high degree and betweenness adaptive attacks in networks with well defined community structures and consequent high modularity.