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A network epidemic model for online community commissioning data

Lookup NU author(s): Dr Clement LeeORCiD, Dr Andy Garbett, Professor Darren Wilkinson

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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.

Publication metadata

Author(s): Lee C, Garbett A, Wilkinson DJ

Publication type: Article

Publication status: Published

Journal: Statistics and Computing

Year: 2018

Volume: 28

Issue: 4

Pages: 891-904

Print publication date: 01/07/2018

Online publication date: 02/08/2017

Acceptance date: 26/07/2017

Date deposited: 08/08/2017

ISSN (print): 0960-3174

ISSN (electronic): 1573-1375

Publisher: Springer New York LLC

URL: https://doi.org/10.1007/s11222-017-9770-6

DOI: 10.1007/s11222-017-9770-6

Data Access Statement: http://dx.doi.org/10.17634/141304-9

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