Abstract
In this chapter, we review the classic susceptible-infected-recovered (SIR) model for disease spread as applied to a social network. In particular, we look at the problem of identifying nodes that are “spreaders” which cause a large part of the population to become infected under this model. To do so, we survey a variety of nodal measures based on centrality (degree, betweenness, etc.) and other methods (shell decomposition, nearest neighbor analysis, etc.). We then present a set of experiments that illustrate the relation of these nodal measures to spreading under the SIR model.
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Notes
- 1.
Technically, the work of Antal et al. [1] proves that the fixation probability for a single mutant invader is proportional to the degree of that node. However, the expected number of mutants, in the limit as time goes to infinity, can simply be computed by multiplying fixation probability by the number of nodes in the network.
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Shakarian, P., Bhatnagar, A., Aleali, A., Shaabani, E., Guo, R. (2015). The SIR Model and Identification of Spreaders. In: Diffusion in Social Networks. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-23105-1_2
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DOI: https://doi.org/10.1007/978-3-319-23105-1_2
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