Algorithms to evaluate the reliability of a network

Galtier, Jérôme; Laugier, Alexandre; Pons, Pascal

doi:10.1109/drcn.2005.1563849

Cited by 14 publications

(9 citation statements)

References 24 publications

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“…Since vertex 6 is in A, component C is a child of A. We find another bi-connected component D = ({10, 11}, 9), and two more mono-connected components E = ({13, 14, 15, 16}, 9) and F = ({12}, 11).…”

Section: Flow Treementioning

confidence: 99%

“…Reliability Bounds. Several lower bounds on (two-terminal) reliability have been defined in the context of communication networks [3], [4], [9], [29]. Such bounds could be used in the place of our sampling approach, to estimate the information gain obtained by adding a network edge to the current active set.…”

Section: Related Workmentioning

confidence: 99%

“…(i) We identify this cycle by considering the previous paths from vertices 14 and 15 to their articulation vertex 9. These paths are (14,13,9) and (15,13,9), respectively. The first common vertex on this path is 13; (ii) We create a new bi-connected component G = ({14, 15}, 13), containing all vertices of this cycle using the first common vertex 13 as articulation vertex.…”

Section: Insertion Examplesmentioning

confidence: 99%

“…For the mono-connected component E Case IVc is used. We identify the path within E that is now involved in a cycle, by using the path (15,13,9) between the involved vertex 15 to articulation Fig. 4.…”

Section: Insertion Examplesmentioning

confidence: 99%

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Efficient Information Flow Maximization in Probabilistic Graphs

Frey¹,

Züfle

Emrich³

et al. 2018

IEEE Trans. Knowl. Data Eng.

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Reliable propagation of information through large networks, e.g. communication networks, social networks or sensor networks is very important in many applications concerning marketing, social networks, and wireless sensor networks. However, social ties of friendship may be obsolete, and communication links may fail, inducing the notion of uncertainty in such networks. In this paper, we address the problem of optimizing information propagation in uncertain networks given a constrained budget of edges. We show that this problem requires to solve two NP-hard subproblems: the computation of expected information flow, and the optimal choice of edges. To compute the expected information flow to a source vertex, we propose the F-tree as a specialized data structure, that identifies independent components of the graph for which the information flow can either be computed analytically and efficiently, or for which traditional Monte-Carlo sampling can be applied independently of the remaining network. For the problem of finding the optimal edges, we propose a series of heuristics that exploit properties of this data structure. Our evaluation shows that these heuristics lead to high quality solutions, thus yielding high information flow, while maintaining low running time.Comment: http://ieeexplore.ieee.org/document/8166795/, IEEE Transactions on Knowledge and Data Engineering (Volume: PP, Issue: 99), Date of Publication: 06 December 201

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Section: Flow Treementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Insertion Examplesmentioning

confidence: 99%

Section: Insertion Examplesmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Information Flow Maximization in Probabilistic Graphs

Frey¹,

Züfle

Emrich³

et al. 2018

IEEE Trans. Knowl. Data Eng.

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“…For undirected networks, factoring with network reductions provides the best possible time (see Ball et al 1995 andGaltier et al 2005 for a comprehensive discussion on the exact methods). The factoring code used in this paper to compute R X follows the procedure given by Page and Perry (1988).…”

Section: Evaluation Of Reliabilitymentioning

confidence: 98%

Efficient Optimization of Reliable Two-Node Connected Networks: A Biobjective Approach

Konak¹,

Smith

2011

INFORMS Journal on Computing

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T his paper presents a biobjective genetic algorithm (GA) to design reliable two-node connected telecommunication networks. Because the exact calculation of the reliability of a network is NP-hard, network designers have been reluctant to use network reliability as a design criterion; however, it is clearly an important aspect. Herein, three methods of reliability assessment are developed: an exact reliability calculation method using factoring, an efficient Monte Carlo estimation procedure using the sequential construction technique and network reductions, and an upper bound for the all-terminal reliability of networks with arbitrary arc reliabilities. These three methods of reliability assessment are used collectively in a biobjective GA with specialized mutation operators that perturb solutions without disturbing two-node connectivity. Computational experiments show that the proposed approach is tractable and significantly improves upon the results found by single-objective heuristics.

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