A network epidemic model for online community commissioning data

Autor: Clement M. Lee, Andrew Garbett, Darren J. Wilkinson
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Statistics and Probability
FOS: Computer and information sciences
Theoretical computer science
MCMC
Computer science
Community commissioning
Preferential attachment
Statistics - Computation
01 natural sciences
Article
Theoretical Computer Science
Set (abstract data type)
Methodology (stat.ME)
010104 statistics & probability
Bernoulli's principle
0103 physical sciences
0101 mathematics
010306 general physics
Statistics - Methodology
Computation (stat.CO)
Random graphs
Random graph
Social and Information Networks (cs.SI)
Social network
business.industry
Computer Science - Social and Information Networks
Statistical model
Stochastic epidemic models
Computer Science::Social and Information Networks
Computational Theory and Mathematics
Identifiability
Statistics
Probability and Uncertainty
Epidemic model
business
Zdroj: Statistics and Computing
ISSN: 0960-3174
Popis: 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.
Comment: 28 pages, 9 figures, 2 tables
Databáze: OpenAIRE
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