Social network analysis via multi-state reliability and conditional influence models

Schneider, Kellie; Rainwater, Chase; Pohl, Edward A.; Hernandez, Ivan; Ramírez-Márquez, José Emmanuel

doi:10.1016/j.ress.2012.07.007

Cited by 32 publications

(15 citation statements)

References 32 publications

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“…Complex networks are consisting of multiple source and destination nodes, complex topology, interdependencies at the component and system levels, and uncertainties in actual conditions of network components and deterioration models [59,60]. According to this definition, lifeline networks such as electrical and gas networks [59,60,63], wireless mobile ad hoc networks (MANETs) [64], wireless mesh networks [65][66][67][68], wireless sensor networks [69,70], sensors based on nanowire networks [71], social networks [72], stochastic-flow manufacturing networks (SMNs) [73], and interconnection networks [10,14,20,32,35] are known as complex network systems from the viewpoint of reliability.…”

Section: Performance Analysismentioning

confidence: 99%

Scalable crossbar network: a non-blocking interconnection network for large-scale systems

Bistouni

Jahanshahi

2014

J Supercomput

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Interconnection networks (INs) are used in wide applications of multiprocessor systems in order to set up connections between various nodes such as processors and memory modules. However, there is a fundamental problem in INs that has always been considered as one of the most challenging issues in this area. Blocking problem in these networks degrades network performance and consequently the performance of the whole system. In the meantime, the main option for dealing with this problem is the use of non-blocking crossbar networks. However, there are engineering and scaling difficulties when using these networks in large-scale systems. The number of pins on a VLSI chip cannot exceed a few hundreds, which restricts the size of the largest crossbar that should be integrated into a single VLSI chip. Using the idea of multistage implementation of crossbar network can resolve the problem. However, the next problem that arises with this idea is high hardware cost. Therefore, in this paper, a new implementation of crossbar network named scalable crossbar network (SCN) that is a non-blocking network is presented to cope with the aforementioned scaling problems. In addition, performance analysis results show that SCN outperforms multistage crossbar networks and multistage interconnection networks in terms of terminal reliability, mean time to failure, and system failure rate.

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Section: Performance Analysismentioning

confidence: 99%

Scalable crossbar network: a non-blocking interconnection network for large-scale systems

Bistouni

Jahanshahi

2014

J Supercomput

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“…Degree centrality measures the direct connection of an individual node in the network (Schneider et al [22]). As a node's number of edges increases, its degree centrality also increases.…”

Section: Social Network Analysis (Sna)mentioning

confidence: 99%

Social network analysis of scientific collaborations across different subject fields

Bozdogan

Akbilgiç

2013

ISU

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This paper analyzes the level of scientific collaboration and interaction in different subject fields using a novel social network analysis (SNA) on a data set provided by the Department of Energy (DOE) Office of Scientific and Technologic Information (OSTI) in Oak Ridge, Tennessee. This paper not only determines the level of scientific collaboration between different disciplines, but it also analyzes the trends among the subject fields considered. The results in this paper show clear pattern recognition discovery on the social network model among key subject fields using weighted networks.

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“…In general, a network is presented by arcs and nodes. To deal with the uncertain situations regarding any probability distribution on the arc in a network, the concept of stochastic flow network (SFN) [1][2][3][4][5][6][7][8][9][10][11][12][13] is applied. One of the most important characteristics for SFN is that the capacity of each arc is a random variable according to a certain probability distribution.…”

Section: Introductionmentioning

confidence: 99%

Reliability Evaluation for a Stochastic Flow Network Based on Upper and Lower Boundary Vectors

Huang

Lin

2019

Mathematics

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For stochastic flow network (SFN), given all the lower (or upper) boundary points, the classic problem is to calculate the probability that the capacity vectors are greater than or equal to the lower boundary points (less than or equal to the upper boundary points). However, in some practical cases, SFN reliability would be evaluated between the lower and upper boundary points at the same time. The evaluation of SFN reliability with upper and lower boundary points at the same time is the focus of this paper. Because of intricate relationships among upper and lower boundary points, a decomposition approach is developed to obtain several simplified subsets. SFN reliability is calculated according to these subsets by means of the inclusion-exclusion principle. Two heuristic options are then established in order to calculate SFN reliability in an efficient direction based on the lower and upper boundary points.

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Social network analysis via multi-state reliability and conditional influence models

Cited by 32 publications

References 32 publications

Scalable crossbar network: a non-blocking interconnection network for large-scale systems

Scalable crossbar network: a non-blocking interconnection network for large-scale systems

Social network analysis of scientific collaborations across different subject fields

Reliability Evaluation for a Stochastic Flow Network Based on Upper and Lower Boundary Vectors

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