Asymptotic Normality of In- and Out-Degree Counts in a Preferential Attachment Model
Probability
2015-10-02 v1 Statistics Theory
Statistics Theory
Abstract
Preferential attachment in a directed scale-free graph is widely used to model the evolution of social networks. Statistical analyses of social networks often relies on node based data rather than conventional repeated sampling. For our directed edge model with preferential attachment, we prove asymptotic normality of node counts based on a martingale construction and a martingale central limit theorem. This helps justify estimation methods based on the statistics of node counts which have specified in-degree and out-degree.
Keywords
Cite
@article{arxiv.1510.00290,
title = {Asymptotic Normality of In- and Out-Degree Counts in a Preferential Attachment Model},
author = {Tiandong Wang and Sidney I. Resnick},
journal= {arXiv preprint arXiv:1510.00290},
year = {2015}
}