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Optimization and Engineering
Article . 2012 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2012
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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“Selfish” algorithm for reducing the computational cost of the network survivability analysis

Authors: Svetlana V. Poroseva;

“Selfish” algorithm for reducing the computational cost of the network survivability analysis

Abstract

In Nature, the primary goal of any network is to survive. This is less obvious for engineering networks (electric power, gas, water, transportation systems etc.) that are expected to operate under normal conditions most of time. As a result, the ability of a network to withstand massive sudden damage caused by adverse events (or survivability) has not been among traditional goals in the network design. Reality, however, calls for the adjustment of design priorities. As modern networks develop toward increasing their size, complexity, and integration, the likelihood of adverse events increases too due to technological development, climate change, and activities in the political arena among other factors. Under such circumstances, a network failure has an unprecedented effect on lives and economy. To mitigate the impact of adverse events on the network operability, the survivability analysis must be conducted at the early stage of the network design. Such analysis requires the development of new analytical and computational tools. Computational analysis of the network survivability is the exponential time problem at least. The current paper describes a new algorithm, in which the reduction of the computational complexity is achieved by mapping an initial network topology with multiple sources and sinks onto a set of simpler smaller topologies with multiple sources and a single sink. Steps for further reducing the time and space expenses of computations are also discussed.

31 pages, 2 tables, 7 figures

Related Organizations
Keywords

Social and Information Networks (cs.SI), FOS: Computer and information sciences, Physics - Physics and Society, Statistical Mechanics (cond-mat.stat-mech), FOS: Physical sciences, Computer Science - Social and Information Networks, Physics and Society (physics.soc-ph), Condensed Matter - Statistical Mechanics

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    popularity
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citations
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!
2
Average
Average
Average