No abstract

No abstract

The dependency link has an important role in the robustness of interdependent networks, which is assigned by the degree, the betweenness and random coupling in previous works. To enhance the robustness and overcome the limitation in existing methods, coupling preferences with the harmonic closeness are proposed, i.e., assortative coupling with the harmonic closeness (ACH) and disassortative coupling with the harmonic closeness (DCH). We investigate cascading failures on interdependent scale-free networks with different values of the average degree and the coupling strength under intentional attacks. We find that interdependent scale-free networks with DCH are more robust than the ones with ACH. Moreover, increasing the coupling strength leads to the more serious cascading failures. Another striking finding is that our method significantly reduces the impact of cascading failures compared with the methods concerning the degree and the betweenness in interdependent scale-free networks regardless of the average degree and the coupling strength. These findings may be useful for the construction of the dependency links in interdependent scale-free networks to resist the cascading propagation.

No abstract

Many studies on cascading failures adopt the degree or the betweenness of a node to define its load. From a novel perspective, we propose an approach to obtain initial loads considering the harmonic closeness and the impact of neighboring nodes. Based on simulation results for different adjustable parameter θ, local parameter δ and proportion of attacked nodes f, it is found that in scale-free networks (SF networks), small-world networks (SW networks) and Erdos-Renyi networks (ER networks), there exists a negative correlation between optimal θ and δ. By the removal of the low load node, cascading failures are more likely to occur in some cases. In addition, we find a valuable result that our method yields better performance compared with other methods in SF networks with an arbitrary f, SW and ER networks with large f. Moreover, the method concerning the harmonic closeness makes these three model networks more robust for different average degrees. Finally, we perform the simulations on twenty real networks, whose results verify that our method is also effective to distribute the initial load in different real networks.