Scaling of percolation transitions on Erdös-Rényi networks under centrality-based attacks

Abstract

The study of network robustness focuses on the way the overall functionality of a network is affected as some of its constituent parts fail. Failures can occur at random or be part of an intentional attack and, in general, networks behave differently against different removal strategies. Although much effort has been put on this topic, there is no unified framework to study the problem. While random failures have been mostly studied under percolation theory, targeted attacks have been recently restated in terms of network dismantling. In this work, we link these two approaches by performing a finite-size scaling analysis to four dismantling strategies over Erdös-Rényi networks: initial and recalculated high degree removal and initial and recalculated high betweenness removal. We find that the critical exponents associated with the initial attacks are consistent with the ones corresponding to random percolation, while the recalculated attacks are likely to belong to different universality classes. In particular, recalculated betweenness produces a very abrupt transition with a hump in the cluster size distribution near the critical point, resembling some explosive percolation processes.

arXiv comment9 pages, 8 figures, 1 table

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Cited by in corpus (8)

• Percolation on complex networks: Theory and application
• Percolation on feature-enriched interconnected systems
• Shortest-path percolation on random networks
• Dismantling Complex Networks by a Neural Model Trained from Tiny Networks
• Critical behaviors of high-degree adaptive and collective-influence percolation
• Explosive dismantling of two-dimensional random lattices under betweenness centrality attacks
• A machine learning procedure to detect network attacks
• Revisiting finite size effect of percolation in degree correlated networks