An Efficient Approach to Enhance the Robustness of Scale-Free Networks

In this paper, we aim to increase the robustness of Scale-Free Networks (SFNs) that are robust against random attacks; however, they are fragile to malicious attacks. When the highest-degree nodes are removed from the SFNs, they greatly affect the connectivity of the remaining nodes. In the proposed method, we perform two attacks, namely, the high degree link attacks and the recalculated high degree link attacks. These link attacks affect the core part of the network. To measure the extent of damaging the network at a greater portion, we use the closeness centrality and the eigenvector centrality. The goal is to construct a topology that shows better robustness against different malicious attacks with a minimum computational cost. The Pearson's correlation coefficient is used to select the measures that are positively correlated with each other. Based on these centrality measures, we optimize the network robustness for both node and link attacks simultaneously. The optimization is performed based on two efficient measures, which are computationally less expensive to increase the network robustness.