Degree-based approximations for network reliability polynomials

Abstract Two approximations for network reliability polynomials, only based upon the knowledge of the degree vector of the graph, are compared: the first-order approximation by Brown et al. and our stochastic approximation. Our method is an extension of the connectivity probability of Erdős–Rènyi random graphs. Both approximations are shown to upper bound the actual reliability polynomial and are increasingly accurate for dense and large graphs. Moreover, the first-order approximation is always sharper or at least as good as the stochastic approximation, whereas the stochastic approximation is computationally easier. Our stochastic approximation (2.2) can determine the critical operational probability under which the graph is disconnected almost surely for any graph and an approximation for the number $ F_{j} $ of sets of $ j $ links whose removal retains the graph $ G $ connected, which is helpfull because the exact computation of $ F_{j} $ is NP-hard.

Authors

Publication Details

Journal
Journal of Complex Networks
Published
2025-03-17
DOI
https://doi.org/10.1093/comnet/cnaf001
Citations
1
Primary Topic
Reliability and Maintenance Optimization
Type
article
Field-Weighted Citation Impact
1.30

Funders

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article

Degree-based approximations for network reliability polynomials

Piet Van Mieghem, Xinhan Liu
1 citations
Journal of Complex Networks
Reliability and Maintenance Optimization
1.30
article

Degree-based approximations for network reliability polynomials

Piet Van Mieghem, Xinhan Liu
article en
1 citations

Abstract

Abstract Two approximations for network reliability polynomials, only based upon the knowledge of the degree vector of the graph, are compared: the first-order approximation by Brown et al. and our stochastic approximation. Our method is an extension of the connectivity probability of Erdős–Rènyi random graphs. Both approximations are shown to upper bound the actual reliability polynomial and are increasingly accurate for dense and large graphs. Moreover, the first-order approximation is always sharper or at least as good as the stochastic approximation, whereas the stochastic approximation is computationally easier. Our stochastic approximation (2.2) can determine the critical operational probability under which the graph is disconnected almost surely for any graph and an approximation for the number $ F_{j} $ of sets of $ j $ links whose removal retains the graph $ G $ connected, which is helpfull because the exact computation of $ F_{j} $ is NP-hard.

Journal of Complex NetworksVol. 13(2)
H2020 European Research Council
Openalex Percentile: Top 23%
Reliability and Maintenance Optimization
1.30
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