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A solution techniqu...
A solution technique to cascading link failure prediction
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- Nakhaei, Niknaz (author)
- Faculty of New Science and Technologies, University of Tehran, Tehran, Iran
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- Ebrahimi, Morteza (author)
- Faculty of New Science and Technologies, University of Tehran, Tehran, Iran
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- Hosseini, S. Ahmad (author)
- Umeå universitet,Institutionen för datavetenskap,Centre for Information Technologies and Applied Mathematics, University of Nova Gorica, Slovenia
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(creator_code:org_t)
- Elsevier BV, 2022
- 2022
- English.
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In: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 258
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- The study of complex networks is a new powerful tool that can provide a profitable skeleton to better elucidate technology-related phenomena and interactions between components of real-world networks However, it is not easy to predict the communal behavior of such systems from their elements and on the other hand, the failure of one or few elements can trigger the failure of other elements throughout the network, resulting in network breakdown and catastrophic events at large scales. Therefore, developing predictive mathematical techniques to examine complex networks is one of the biggest challenges of the present time. Knowing that link failure prediction is less studied in the OR literature, the present study articulates a method to predict link failures in complex networks, which is primarily based on Bayesian Belief Networks (BBN) and TOPSIS. The method aims to predict failures based on the affective factors of failures in networks. To this end, effective factors of failures are first detected, and then the graph of the relationship of factors along with their weight is determined. After all, the method provides the prediction for future damaged components. The functionality of the method is validated by an extensive computational analysis employing simulation in scale-free, random, and actual international aviation networks and its performance is compared with other benchmark algorithms. The results and sensitivity analysis experiments arrive at prominent managerial insights and efficacious implications and show that our method can generate high-quality solutions in many networks.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Bayesian Belief Networks
- Cascading failure
- Complex networks
- Failure prediction
- Operations Research
Publication and Content Type
- ref (subject category)
- art (subject category)
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