Deterministic multidimensional growth model for small-world networks

TL;DR

This paper introduces a deterministic multidimensional growth model for small-world networks that accurately captures key properties like clustering, path length, and degree distribution, aligning well with real-world network behaviors.

Contribution

The paper presents a novel deterministic model that characterizes small-world properties with precise analytical expressions for key network metrics.

Findings

Model exhibits small-world effect with high clustering and short path length

Derived accurate analytical formulas for degree distribution and network diameter

Numerical and experimental verification confirms model's effectiveness

Abstract

We proposed a deterministic multidimensional growth model for small-world networks. The model can characterize the distinguishing properties of many real-life networks with geometric space structure. Our results show the model possesses small-world effect: larger clustering coefficient and smaller characteristic path length. We also obtain some accurate results for its properties including degree distribution, clustering coefficient and network diameter and discuss them. It is also worth noting that we get an accurate analytical expression for calculating the characteristic path length. We verify numerically and experimentally these main features.