Scaling properties in spatial networks and its effects on topology and traffic dynamics

TL;DR

This paper investigates how the power-law distribution of distances in spatial networks influences their topology and traffic dynamics, revealing optimal parameters for shortest paths and traffic efficiency.

Contribution

It introduces a spatial network model with power-law distributed long-range connections and analyzes how the exponent affects network structure and traffic performance.

Findings

Smallest average shortest path at δ=2

Best traffic process at δ=1.5

Deep understanding of spatial structure and network function

Abstract

Empirical studies on the spatial structures in several real transport networks reveal that the distance distribution in these networks obeys power law. To discuss the influence of the power-law exponent on the network's structure and function, a spatial network model is proposed. Based on a regular network and subject to a limited cost $C$, long range connections are added with power law distance distribution $P(r)=ar^{-\delta}$. Some basic topological properties of the network with different $\delta$ are studied. It is found that the network has the smallest average shortest path when $\delta=2$. Then a traffic model on this network is investigated. It is found that the network with $\delta=1.5$ is best for the traffic process. All of these results give us some deep understandings about the relationship between spatial structure and network function.