Critical and nonpercolating phases in bond percolation on the Song–Havlin–Makse network

Abstract We investigate bond percolation on the Song–Havlin–Makse (SHM) network, a scale-free tree with a tunable degree exponent and dimensionality. Using a generating function approach, we analytically derive the average size and the fractal exponent of the root cluster for deterministic cases. Our analysis reveals that bond percolation on the SHM network remains in a non-percolating phase for all p < 1 when the network is fractal (i.e. finite-dimensional), whereas it exhibits a critical phase, where the cluster size distribution follows a power-law with a p -dependent exponent, throughout the entire range of p when the network is small-world (i.e. infinite-dimensional), regardless of the specific dimensionality or degree exponent. The analytical results are in excellent agreement with Monte Carlo simulations.

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Publication Details

Journal
Journal of Statistical Mechanics Theory and Experiment
Published
2025-10-01
DOI
https://doi.org/10.1088/1742-5468/ae042a
Primary Topic
Theoretical and Computational Physics
Type
article
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article

Critical and nonpercolating phases in bond percolation on the Song–Havlin–Makse network

Takehisa Hasegawa, Kazuki Wataya
Journal of Statistical Mechanics Theory and Experiment
Theoretical and Computational Physics
article

Critical and nonpercolating phases in bond percolation on the Song–Havlin–Makse network

Takehisa Hasegawa, Kazuki Wataya
article en

Abstract

Abstract We investigate bond percolation on the Song–Havlin–Makse (SHM) network, a scale-free tree with a tunable degree exponent and dimensionality. Using a generating function approach, we analytically derive the average size and the fractal exponent of the root cluster for deterministic cases. Our analysis reveals that bond percolation on the SHM network remains in a non-percolating phase for all p < 1 when the network is fractal (i.e. finite-dimensional), whereas it exhibits a critical phase, where the cluster size distribution follows a power-law with a p -dependent exponent, throughout the entire range of p when the network is small-world (i.e. infinite-dimensional), regardless of the specific dimensionality or degree exponent. The analytical results are in excellent agreement with Monte Carlo simulations.

Journal of Statistical Mechanics Theory and ExperimentVol. 2025(10)
Ibaraki University (JP)
Openalex Percentile: Top 83%
Theoretical and Computational Physics
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