A Review of Metrics, Algorithms and Methodologies for Network Reliability

Gaur, Vaibhav; Yadav, Om Prakash; Soni, Gunjan; Rathore, Ajay Pal Singh

doi:10.1109/ieem44572.2019.8978688

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…It is also necessary to point out that e-Lattes identifies, from a Lattes CV set considered, two researchers who co-authored at least one article, creating an endogenous co-authorship network that enables the computation of diverse network metrics (Gaur et al, 2019).…”

Section: About E-lattesmentioning

confidence: 99%

Scientometric Analysis of Research Output from Brazil in Response to the Zika Crisis Using e-Lattes

Sampaio¹,

Batista

Barreto³

et al. 2020

Journal of Data and Information Science

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PurposeThis paper aims to test the use of e-Lattes to map the Brazilian scientific output in a recent research health subject: Zika Virus.Design/methodology/approachFrom a set of Lattes CVs of Zika researchers registered on the Lattes Platform, we used the e-Lattes to map the Brazilian scientific response to the Zika crisis.FindingsBrazilian science articulated quickly during the public health emergency of international concern (PHEIC) due to the creation of mechanisms to streamline funding of scientific research.Research limitationsWe did not assess any dimension of research quality, including the scientific impact and societal value.Practical implicationse-Lattes can provide useful guidelines for different stakeholders in research groups from Lattes CVs of members.Originality/valueThe information included in Lattes CVs permits us to assess science from a broader perspective taking into account not only scientific research production but also the training of human resources and scientific collaboration.

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Section: About E-lattesmentioning

confidence: 99%

Scientometric Analysis of Research Output from Brazil in Response to the Zika Crisis Using e-Lattes

Sampaio¹,

Batista

Barreto³

et al. 2020

Journal of Data and Information Science

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“…e analytic methods for calculating k-terminal network reliability are NP-hard [26][27][28][29][30], and the computation time grows exponentially with the increase of node or link numbers. To overcome this problem, several approximation methods have been put forward, and so far, the neural network-based approach is believed to be the most efficient and promising among them [31,32].…”

Section: Introductionmentioning

confidence: 99%

A Graph Convolution Neural Network-Based Framework for Communication Network K-Terminal Reliability Estimation

Zhang

Cao³

et al. 2022

Security and Communication Networks

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The exact computation of network k-terminal reliability is an NP-hard problem, and many approximation methods have been proposed as alternatives, among which the neural network-based approaches are believed to be the most effective and promising. However, the existing neural network-based methods either ignore the local structures in the network topology or process the local structures as Euclidean data, while the network topology represented by the graph is in fact non-Euclidean. Seeing that the Graph Convolution Neural network (GCN) is a generalization of convolution operators onto non-Euclidean data structure, in an effort to fill in the gap, this paper proposes a GCN-based framework for the estimation of communication network reliability. First, a dataset with sufficient sample size is constructed, by calculating the k-terminal reliability via the exact contraction-deletion method for the generated network samples. Then, an estimation model based on GCN is built, where several graph convolution layers process input information and extract node-level structural features from the network topology, a concatenation layer fuses the structural features into a graph-level representation feature, and a multi-layer perceptron computes the k-terminal reliability as output. To demonstrate the practicality and rationality of our proposed model, comparative experiments are carried out on 12 datasets, the results of which show that our proposed GCN model has an average of 59.60% and 57.52% improvement over existing methods on homogeneous datasets and heterogeneous datasets, respectively.

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“…The major factors affecting the network availability from different layers are classified in order to indicate their impact. A review has recently been published concerning the probability methods in [25], where the most important trends and achievements in network reliability algorithms are presented.…”

Section: Introductionmentioning

confidence: 99%

Hybrid algorithm for two-terminal reliability evaluation in communication networks

Mahmood

Uçan

Zaidan

et al. 2021

IJEECS

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<span>Network reliability is valuable in establishing a survivable communication network. Reliability evaluation algorithms are used in the design stage and during the network deployment. This work presents a new multistage hybrid technique for two-terminal reliability evaluation problem. It is based on a combination of graph reduction techniques and tie-set method. A new approach has been introduced for deducing tie-sets in a network containing both unidirectional and bi-directional edges. The proposed algorithm can be applied for both simple and complex networks without restrictions. The results confirm that new algorithm evaluates network's reliability with decreasing computing time compared to classical algorithms. The results for a case study of a 20-node network have demonstrated that the required time for reliability evaluation is decreased from (t>1 hour) in the case of using a classical algorithm, to (t<1 second) for the new algorithm.</span>

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A Review of Metrics, Algorithms and Methodologies for Network Reliability

Cited by 5 publications

References 25 publications

Scientometric Analysis of Research Output from Brazil in Response to the Zika Crisis Using e-Lattes

Scientometric Analysis of Research Output from Brazil in Response to the Zika Crisis Using e-Lattes

A Graph Convolution Neural Network-Based Framework for Communication Network K-Terminal Reliability Estimation

Hybrid algorithm for two-terminal reliability evaluation in communication networks

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