Degree Growth Rates and Index Estimation in a Directed Preferential Attachment Model

TL;DR

This paper analyzes the joint growth of in- and out-degrees in directed preferential attachment models, establishing the convergence of empirical measures and the consistency of the Hill estimator for tail exponents.

Contribution

It provides the first theoretical analysis of joint degree growth and proves the consistency of the Hill estimator in directed preferential attachment models.

Findings

Convergence of the joint empirical measure for in- and out-degrees.

Asymptotic behavior of joint degree sequences via switched birth processes.

Consistency of Hill estimators for tail exponents.

Abstract

Preferential attachment is widely used to model power-law behavior of degree distributions in both directed and undirected networks. In a directed preferential attachment model, despite the well-known marginal power-law degree distributions, not much investigation has been done on the joint behavior of the in- and out-degree growth. Also, statistical estimates of the marginal tail exponent of the power-law degree distribution often use the Hill estimator as one of the key summary statistics, even though no theoretical justification has been given. This paper focuses on convergence of the joint empirical measure for in- and out-degrees and proves the consistency of the Hill estimator. To do this, we first derive the asymptotic behavior of the joint degree sequences by embedding the in- and out-degrees of a fixed node into a pair of switched birth processes with immigration and then establish the convergence of the joint tail empirical measure. From these steps, the consistency of the Hill estimators is obtained.