An Expectation Maximization Framework for Yule-Simon Preferential Attachment Models
This work offers a methodological improvement for estimating parameters in preferential attachment models, which is incremental as it adapts an existing EM framework to a specific distribution.
The paper tackles parameter estimation for the Yule-Simon distribution, which models 'rich get richer' effects common in industrial settings, by developing an Expectation Maximization algorithm that provides frequentist and Bayesian estimates with standard errors and proven convergence rates.
In this paper we develop an Expectation Maximization(EM) algorithm to estimate the parameter of a Yule-Simon distribution. The Yule-Simon distribution exhibits the "rich get richer" effect whereby an 80-20 type of rule tends to dominate. These distributions are ubiquitous in industrial settings. The EM algorithm presented provides both frequentist and Bayesian estimates of the $λ$ parameter. By placing the estimation method within the EM framework we are able to derive Standard errors of the resulting estimate. Additionally, we prove convergence of the Yule-Simon EM algorithm and study the rate of convergence. An explicit, closed form solution for the rate of convergence of the algorithm is given. Applications including graph node degree distribution estimation are listed.