Random Processes with High Variance Produce Scale Free Networks
Document Type
Article
Publication Date
10-15-2022
Abstract
Real-world networks tend to be scale free, having heavy-tailed degree distributions with more hubs than predicted by classical random graph generation methods. Preferential attachment and growth are the most commonly accepted mechanisms leading to these networks and are incorporated in the Barabási–Albert (BA) model (Barabási, 2009 [1]). We provide an alternative model using a randomly stopped linking process inspired by a generalized Central Limit Theorem (CLT) for geometric distributions with widely varying parameters. The common characteristic of both the BA model and our randomly stopped linking model is the mixture of widely varying geometric distributions, suggesting the critical characteristic of scale free networks is high variance, not growth or preferential attachment. The limitation of classical random graph models is low variance in parameters, while scale free networks are the natural, expected result of real-world variance.
Publication Information
Johnston, Josh and Andersen, Tim. (2022). "Random Processes with High Variance Produce Scale Free Networks". Physica A: Statistical Mechanics and Its Applications, 604, 127588. https://doi.org/10.1016/j.physa.2022.127588