TY - JOUR
T1 - Critical behaviors of high-degree adaptive and collective-influence percolation
AU - Kim, Jung Ho
AU - Kim, Soo Jeong
AU - Goh, K. I.
N1 - Funding Information:
This work was supported in part by the National Research Foundation of Korea (NRF) Grants funded by the Korea government (MSIT) (Nos. 2017R1A2B2003121 and 2020R1A2C2003669).
PY - 2020/7/1
Y1 - 2020/7/1
N2 - 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.
AB - 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.
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U2 - 10.1063/1.5139454
DO - 10.1063/1.5139454
M3 - Article
C2 - 32752629
AN - SCOPUS:85089131093
VL - 30
JO - Chaos
JF - Chaos
SN - 1054-1500
IS - 7
M1 - 73131
ER -