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 propogation 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.