Adding links on minimum degree and longest distance strategies for improving network robustness and efficiency

Author:

Chujyo MasakiORCID,Hayashi Yukio

Abstract

Many real-world networks characterized by power-law degree distributions are extremely vulnerable against malicious attacks. Therefore, it is important to obtain effective methods for strengthening the robustness of the existing networks. Previous studies have been discussed some link addition methods for improving the robustness. In particular, two effective strategies for selecting nodes to add links have been proposed: the minimum degree and longest distance strategies. However, it is unclear whether the effects of these strategies on the robustness are independent or not. In this paper, we investigate the contributions of these strategies to improving the robustness by adding links in distinguishing the effects of degrees and distances as much as possible. Through numerical simulation, we find that the robustness is effectively improved by adding links on the minimum degree strategy for both synthetic trees and real networks. As an exception, only when the number of added links is small, the longest distance strategy is the best. Conversely, the robustness is only slightly improved by adding links on the shortest distance strategy in many cases, even combined with the minimum degree strategy. Therefore, enhancing global loops is essential for improving the robustness rather than local loops.

Funder

Japan Society for the Promotion of Science

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference43 articles.

1. Mean-field theory for scale-free random networks;AL Barabási;Physica A: Statistical Mechanics and its Applications,1999

2. Error and attack tolerance of complex networks;R Albert;nature,2000

3. Mitigation of malicious attacks on networks;CM Schneider;Proceedings of the National Academy of Sciences,2011

4. Robustness of onionlike correlated networks against targeted attacks;T Tanizawa;Phys Rev E,2012

5. Onion-like networks are both robust and resilient;Y Hayashi;Scientific reports,2018

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