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Corrigendum to "Degree-Based Approximations for Network Reliability Polynomials". Comment on J. Complex Networks 2025, 13, cnaf001

Physics and Society 2025-10-09 v1 Applied Physics

Abstract

Our original paper \cite{VanMieghem2025} described the stochastic approximation relG(p)=[1ϕD(1p)]N\overline{rel}_G(p)=\bigl[1-\phi_D(1-p)\bigr]^{N} in \cite[eq. (2.2)]{VanMieghem2025} and the first-order approximation (R1)G(p)=i=1N ⁣[1(1p)di](R_1)_G(p)=\prod_{i=1}^{N}\!\bigl[1-(1-p)^{d_i}\bigr] in \cite[eq. (4.1)]{VanMieghem2025} as upper bounds for the all-terminal reliability polynomial relG(p)rel_G(p). The present corrigendum clarifies that the unique upper bound is Pr[D^min1]\Pr[\hat D_{\min}\geq 1], which is difficult to compute exactly, because we must account for correlated node-isolation events. Both the stochastic approximation relG\overline{rel}_G and the first-order approximation (R1)G(R_1)_G ignore those correlations, assume independence and, consequently, do not always upperbound relG(p)rel_G(p) as stated previously. The complete graph K3K_{3} is a counterexample, where both approximations lie below the exact reliability polynomial relK3(p)rel_{K_3}(p), illustrating that they are not upper bounds. Moreover, as claimed in \cite{VanMieghem2025}, the first-order approximation (R1)G(R_1)_G is not always more accurate than the stochastic approximation relG\overline{rel}_G. We show by an example that the relative accuracy of the stochastic approximation relG\overline{rel}_G and the first-order approximation (R1)G(R_1)_G varies with the graph GG and the link operational probability pp. }{network robustness, node failure, probabilistic graph, reliability polynomial

Cite

@article{arxiv.2510.06247,
  title  = {Corrigendum to "Degree-Based Approximations for Network Reliability Polynomials". Comment on J. Complex Networks 2025, 13, cnaf001},
  author = {Xinhan Liu and Piet Van Mieghem},
  journal= {arXiv preprint arXiv:2510.06247},
  year   = {2025}
}
R2 v1 2026-07-01T06:22:11.161Z