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A network epidemic model for online community commissioning data

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A network epidemic model for online community commissioning data. / Lee, Clement; Garbett, Andrew; Wilkinson, Darren J.
In: Statistics and Computing, Vol. 28, No. 4, 01.07.2018, p. 891-904.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Lee, C, Garbett, A & Wilkinson, DJ 2018, 'A network epidemic model for online community commissioning data', Statistics and Computing, vol. 28, no. 4, pp. 891-904. https://doi.org/10.1007/s11222-017-9770-6

APA

Lee, C., Garbett, A., & Wilkinson, D. J. (2018). A network epidemic model for online community commissioning data. Statistics and Computing, 28(4), 891-904. https://doi.org/10.1007/s11222-017-9770-6

Vancouver

Lee C, Garbett A, Wilkinson DJ. A network epidemic model for online community commissioning data. Statistics and Computing. 2018 Jul 1;28(4):891-904. Epub 2017 Aug 2. doi: 10.1007/s11222-017-9770-6

Author

Lee, Clement ; Garbett, Andrew ; Wilkinson, Darren J. / A network epidemic model for online community commissioning data. In: Statistics and Computing. 2018 ; Vol. 28, No. 4. pp. 891-904.

Bibtex

@article{2fc8faeb572642a8bfa4fec2437965b7,
title = "A network epidemic model for online community commissioning data",
abstract = "A statistical model assuming a preferential attachment network, which is generated by adding nodes sequentially according to a few simple rules, usually describes real-life networks better than a model assuming, for example, a Bernoulli random graph, in which any two nodes have the same probability of being connected, does. Therefore, to study the propagation of “infection” across a social network, we propose a network epidemic model by combining a stochastic epidemic model and a preferential attachment model. A simulation study based on the subsequent Markov Chain Monte Carlo algorithm reveals an identifiability issue with the model parameters. Finally, the network epidemic model is applied to a set of online commissioning data.",
keywords = "Stochastic epidemic models, MCMC, Random graphs, Preferential attachment, Community commissioning",
author = "Clement Lee and Andrew Garbett and Wilkinson, {Darren J.}",
year = "2018",
month = jul,
day = "1",
doi = "10.1007/s11222-017-9770-6",
language = "English",
volume = "28",
pages = "891--904",
journal = "Statistics and Computing",
issn = "0960-3174",
publisher = "Springer Netherlands",
number = "4",

}

RIS

TY - JOUR

T1 - A network epidemic model for online community commissioning data

AU - Lee, Clement

AU - Garbett, Andrew

AU - Wilkinson, Darren J.

PY - 2018/7/1

Y1 - 2018/7/1

N2 - A statistical model assuming a preferential attachment network, which is generated by adding nodes sequentially according to a few simple rules, usually describes real-life networks better than a model assuming, for example, a Bernoulli random graph, in which any two nodes have the same probability of being connected, does. Therefore, to study the propagation of “infection” across a social network, we propose a network epidemic model by combining a stochastic epidemic model and a preferential attachment model. A simulation study based on the subsequent Markov Chain Monte Carlo algorithm reveals an identifiability issue with the model parameters. Finally, the network epidemic model is applied to a set of online commissioning data.

AB - A statistical model assuming a preferential attachment network, which is generated by adding nodes sequentially according to a few simple rules, usually describes real-life networks better than a model assuming, for example, a Bernoulli random graph, in which any two nodes have the same probability of being connected, does. Therefore, to study the propagation of “infection” across a social network, we propose a network epidemic model by combining a stochastic epidemic model and a preferential attachment model. A simulation study based on the subsequent Markov Chain Monte Carlo algorithm reveals an identifiability issue with the model parameters. Finally, the network epidemic model is applied to a set of online commissioning data.

KW - Stochastic epidemic models

KW - MCMC

KW - Random graphs

KW - Preferential attachment

KW - Community commissioning

U2 - 10.1007/s11222-017-9770-6

DO - 10.1007/s11222-017-9770-6

M3 - Journal article

VL - 28

SP - 891

EP - 904

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 4

ER -