Crossovers in ScaleFree Networks on Geographical Space

TL;DR

This paper investigates how the topological properties of scale-free networks embedded in geographical space change with the fitness distribution's scaling exponent, revealing two crossover points affecting network structure.

Contribution

It introduces a simple model linking topological properties with vertex attributes in geographical scale-free networks and identifies two crossover points in the scaling exponent.

Findings

Two crossover points identified in the scaling exponent

Topological properties vary with fitness distribution

Model links vertex attributes to network topology

Abstract

Complex networks are characterized by several topological properties: degree distribution, clustering coefficient, average shortest path length, etc. Using a simple model to generate scale-free networks embedded on geographical space, we analyze the relationship between topological properties of the network and attributes (fitness and location) of the vertices in the network. We find there are two crossovers for varying the scaling exponent of the fitness distribution.