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Article . 2024 . Peer-reviewed
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Article . 2024
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Article . 2024
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New approximations for network reliability

Authors: Jason I. Brown; Theodore Kolokolnikov; Robert E. Kooij;

New approximations for network reliability

Abstract

AbstractWe introduce two new methods for approximating the all‐terminal reliability of undirected graphs. First, we introduce an edge removal process: remove edges at random, one at a time, until the graph becomes disconnected. We show that the expected number of edges thus removed is equal to , where is the number of edges in the graph, and is the average of the all‐terminal reliability polynomial. Based on this process, we propose a Monte‐Carlo algorithm to quickly estimate the graph reliability (whose exact computation is NP‐hard). Moreover, we show that the distribution of the edge removal process can be used to quickly approximate the reliability polynomial. We then propose increasingly accurate asymptotics for graph reliability based solely on degree distributions of the graph. These asymptotics are tested against several real‐world networks and are shown to be accurate for sufficiently dense graphs. While the approach starts to fail for “subway‐like” networks that contain many paths of vertices of degree two, different asymptotics are derived for such networks.

Country
Netherlands
Keywords

second order approximation, Network reliability, 511, All terminal reliability, first-order approximation, Reliability, availability, maintenance, inspection in operations research, regular graph, first order approximation, Deterministic network models in operations research, network reliability, Railroads, Approximation, approximation, Monte Carlo, average reliability, Monte Carlo methods, Programming involving graphs or networks, Subway-like network, First-order approximations, Reliability, second-order approximation, Regular graphs, Average reliability, Undirected graphs, subway-like network, Second-order approximation, Asymptotics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Top 10%
Top 10%
Top 10%
hybrid