Reinforcing the Resilience of Complex Networks

TL;DR

This paper explores methods to enhance the resilience of complex networks, such as random and scale-free models, by applying augmentation strategies and evaluates their effectiveness through simulations.

Contribution

It introduces and compares three augmentation schemes to improve network resilience, highlighting the superior robustness of random expansion methods.

Findings

Augmentation significantly increases network resilience.

Random expansion outperforms other schemes in robustness.

Resilience improvements are confirmed through simulation results.

Abstract

Given a connected network, it can be augmented by applying a growing strategy (e.g. random or scale-free rules) over the previously existing structure. Another approach for augmentation, recently introduced, involves incorporating a direct edge between any two nodes which are found to be connected through at least one self-avoiding path of length L. This work investigates the resilience of random and scale-free models augmented by using the three schemes identified above. Considering random and scale-free networks, their giant cluster are identified and reinforced, then the resilience of the resulting networks with respect to highest degree node attack is quantified through simulations. The results, which indicate that substantial reinforcement of the resilience of complex networks can be achieved by the expansions, also confirm the superior robustness of the random expansion.