Deterministic weighted scale-free small-world networks

TL;DR

This paper introduces a deterministic weighted scale-free small-world network model that captures key structural and weight dynamics, effectively mimicking real-world networks with high clustering and stable properties.

Contribution

The authors develop a novel deterministic model with tunable parameters for degree distribution, providing comprehensive insights into topology and weight evolution in weighted scale-free small-world networks.

Findings

Model achieves degree exponent between 2 and 3.

Networks exhibit high, stable clustering coefficients.

Clustering coefficient declines rapidly with size in previous models.

Abstract

We propose a deterministic weighted scale-free small-world model for considering pseudofractal web with the coevolution of topology and weight. In the model, we have the degree distribution exponent $\gamma$ restricted to a range between 2 and 3, simultaneously tunable with two parameters. At the same time, we provide a relatively complete view of topological structure and weight dynamics characteristics of the networks: weight and strength distribution; degree correlations; average clustering coefficient and degree-cluster correlations; as well as the diameter. We show that our model is particularly effective at mimicing weighted scale-free small-world networks with a high and relatively stable clustering coefficient, which rapidly decline with the network size in most previous models.