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A network epidemic model for online community commissioning data

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<mark>Journal publication date</mark>1/07/2018
<mark>Journal</mark>Statistics and Computing
Issue number4
Volume28
Number of pages14
Pages (from-to)891-904
Publication StatusPublished
Early online date2/08/17
<mark>Original language</mark>English

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.