In-Degree and PageRank of Web pages: Why do they follow similar power laws?

TL;DR

This paper introduces a mathematical model explaining why PageRank and In-Degree distributions follow similar power laws, supported by analytical proofs and experimental validation.

Contribution

It presents a novel stochastic model linking PageRank and In-Degree, providing analytical insights into their similar power law behavior.

Findings

PageRank and In-Degree follow similar power laws with the same exponent.

The model analytically explains the tail behavior difference as a multiplicative factor.

Results align well with empirical data.

Abstract

The PageRank is a popularity measure designed by Google to rank Web pages. Experiments confirm that the PageRank obeys a `power law' with the same exponent as the In-Degree. This paper presents a novel mathematical model that explains this phenomenon. The relation between the PageRank and In-Degree is modelled through a stochastic equation, which is inspired by the original definition of the PageRank, and is analogous to the well-known distributional identity for the busy period in the M/G/1 queue. Further, we employ the theory of regular variation and Tauberian theorems to analytically prove that the tail behavior of the PageRank and the In-Degree differ only by a multiplicative factor, for which we derive a closed-form expression. Our analytical results are in good agreement with experimental data.