Growing small-world networks based on a modified BA model

TL;DR

This paper introduces a modified Barabási-Albert model that generates small-world networks with power-law degree distributions by connecting new nodes locally to their creator and neighbors, better reflecting real-world network properties.

Contribution

It presents a simple, modified growth model for small-world networks that incorporates local attachment, capturing both scale-free and small-world features.

Findings

Produces small-world networks with power-law degree distribution

Shows properties similar to real-world networks in clustering and path length

Compared favorably with the original BA model

Abstract

We propose a simple growing model for the evolution of small-world networks. It is introduced as a modified BA model in which all the edges connected to the new nodes are made locally to the creator and its nearest neighbors. It is found that this model can produce small-world networks with power-law degree distributions. Properties of our model, including the degree distribution, clustering, and the average path length are compared with that of the BA model. Since most real networks are both scale-free and small-world networks, our model may provide a satisfactory description for empirical characteristics of real networks.