Reinforcing the resilience of complex networks

Given a connected network, it can be augmented by applying a growing strategy (e.g., random- or preferential-attachment 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 preferential-attachment models augmented by using the three schemes identified above. Considering random- and preferential-attachment 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. Statistical characterization of the effects of augmentations over some of the network properties is also provided. 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. An important obtained result is that the initial growth scheme was found to have little effect over the possibilities of further enhancement of the network by subsequent reinforcement schemes.