Preferential attachment scale-free growth model with random fitness

TL;DR

This paper proposes a new network growth model combining preferential attachment, node fitness, and spatial distance, resulting in a planar network with a power-law distributed spatial placement of nodes.

Contribution

It introduces a novel network growth model integrating fitness and spatial distance into preferential attachment, expanding understanding of scale-free network formation.

Findings

The model produces scale-free networks with realistic spatial properties.

Network degree distribution follows a power-law.

The spatial distribution of nodes influences network topology.

Abstract

We introduce a network growth model in which the preferential attachment probability includes the fitness vertex and the Euclidean distance between nodes. We grow a planar network around its barycenter. Each new site is fixed in space by obeying a power law distribution.