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.

Highlights

  • Enhancing the ability of real-world networks to resist cascading failures is a hot topic and many scholars have paid a lot of attention to it

  • For most of the studies on cascading failures, initial loads on nodes are determined by their degree or betweenness

  • We propose an approach to obtain initial loads by means of the harmonic closeness and the knowledge of adjacent nodes

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Summary

OPEN ACCESS

Data Availability Statement: The data underlying the results presented in the study are available from the following sources: https://gephi.org/ datasets/eurosis.gexf.zip http://networkrepository. Com/fb-pages-company.php https://gephi.org/ datasets/airlines.graphml.zip http:// networkrepository.com/power-494-bus.php http:// networkrepository.com/power-662-bus.php http:// networkrepository.com/inf-euroroad.php http:// networkrepository.com/power-1138-bus.php http://networkrepository.com/power-bcspwr.

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Author Contributions

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