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Asymptotic normality of degree counts in a preferential attachment model
Published online by Cambridge University Press: 25 July 2016
Abstract
Preferential attachment is a widely adopted paradigm for understanding the dynamics of social networks. Formal statistical inference, for instance GLM techniques, and model-verification methods will require knowing test statistics are asymptotically normal even though node- or count-based network data are nothing like classical data from independently replicated experiments. We therefore study asymptotic normality of degree counts for a sequence of growing simple undirected preferential attachment graphs. The methods of proof rely on identifying martingales and then exploiting the martingale central limit theorems.
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- Research Article
- Information
- Advances in Applied Probability , Volume 48 , Issue A: Probability, Analysis and Number Theory , July 2016 , pp. 283 - 299
- Copyright
- Copyright © Applied Probability Trust 2016
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