Critical behaviors of high-degree adaptive and collective-influence percolation

TL;DR

This study compares the critical behaviors of high-degree adaptive and collective-influence percolation strategies on networks, revealing they exhibit standard mean-field criticality despite causing abrupt network collapses.

Contribution

It provides a detailed analysis of the critical exponents and behaviors of degree-based and CI-based percolation, highlighting their similarities in mean-field universality class.

Findings

Both attack strategies show mean-field critical behavior at percolation transition.

CI-based attack causes more abrupt network disintegration.

Degeneracy in top-centrality nodes may influence percolation dynamics.

Abstract

How the giant component of a network disappears under attacking nodes or links addresses a key aspect of network robustness, which can be framed into percolation problems. Various strategies to select the node to be deactivated have been studied in the literature; for instance, a simple random failure or high-degree adaptive (HDA) percolation. Recently a new attack strategy based on a quantity called collective-influence (CI) has been proposed from the perspective of optimal percolation. By successively deactivating the node having the largest CI-centrality value, it was shown to be able to dismantle a network more quickly and abruptly than many of the existing methods. In this paper, we focus on the critical behaviors of the percolation processes following degree-based attack and CI-based attack on random networks. Through extensive Monte Carlo simulations assisted by numerical solutions, we estimate various critical exponents of the HDA percolation and those of the CI percolations. Our results show that these attack-type percolation processes, despite displaying apparently more abrupt collapse, nevertheless exhibit standard mean-field critical behaviors at the percolation transition point. We further discover an extensive degeneracy in top-centrality nodes in both processes, which may provide a hint for understanding the observed results