Crossover from Scale-Free to Spatial Networks

TL;DR

This paper investigates how networks transition from scale-free to spatial structures as the interaction range decreases, revealing effects on degree distribution, clustering, and assortativity, with implications for real-world network formation.

Contribution

It introduces a model combining preferential attachment and distance-based selection, analyzing the crossover and scaling behaviors between network types.

Findings

Connectivity distribution shows a cutoff depending on node density.

High clustering coefficient observed in spatial networks.

Positive degree correlation (assortativity) matches empirical data.

Abstract

In many networks such as transportation or communication networks, distance is certainly a relevant parameter. In addition, real-world examples suggest that when long-range links are existing, they usually connect to hubs-the well connected nodes. We analyze a simple model which combine both these ingredients--preferential attachment and distance selection characterized by a typical finite `interaction range'. We study the crossover from the scale-free to the `spatial' network as the interaction range decreases and we propose scaling forms for different quantities describing the network. In particular, when the distance effect is important (i) the connectivity distribution has a cut-off depending on the node density, (ii) the clustering coefficient is very high, and (iii) we observe a positive maximum in the degree correlation (assortativity) which numerical value is in agreement with empirical measurements. Finally, we show that if the number of nodes is fixed, the optimal network which minimizes both the total length and the diameter lies in between the scale-free and spatial networks. This phenomenon could play an important role in the formation of networks and could be an explanation for the high clustering and the positive assortativity which are non trivial features observed in many real-world examples.