A steady state model for graph power laws

TL;DR

This paper introduces a novel web graph model that produces power law distributions without relying on incremental growth, challenging previous assumptions about the necessity of preferential connectivity and growth.

Contribution

The paper presents a new steady state web graph model that generates power law distributions without incremental growth, and compares its clustering behavior with existing models.

Findings

The proposed model successfully produces power law distributions without growth.

It accurately predicts web graph clustering behavior.

It challenges the belief that growth and preferential attachment are necessary for power laws.

Abstract

Power law distribution seems to be an important characteristic of web graphs. Several existing web graph models generate power law graphs by adding new vertices and non-uniform edge connectivities to existing graphs. Researchers have conjectured that preferential connectivity and incremental growth are both required for the power law distribution. In this paper, we propose a different web graph model with power law distribution that does not require incremental growth. We also provide a comparison of our model with several others in their ability to predict web graph clustering behavior.