Self-similar scaling of density in complex real-world networks

TL;DR

This paper demonstrates that the density of complex real-world networks follows a universal power-law scaling under various renormalization methods, revealing a scale-free density structure across different network types and scales.

Contribution

It provides empirical evidence of a universal power-law density scaling in diverse real-world networks using multiple renormalization techniques.

Findings

Density scales as a power-law with network size under renormalization

Universal scaling observed across different network types

Supports the concept of scale-free density in complex networks

Abstract

Despite their diverse origin, networks of large real-world systems reveal a number of common properties including small-world phenomena, scale-free degree distributions and modularity. Recently, network self-similarity as a natural outcome of the evolution of real-world systems has also attracted much attention within the physics literature. Here we investigate the scaling of density in complex networks under two classical box-covering renormalizations-network coarse-graining-and also different community-based renormalizations. The analysis on over 50 real-world networks reveals a power-law scaling of network density and size under adequate renormalization technique, yet irrespective of network type and origin. The results thus advance a recent discovery of a universal scaling of density among different real-world networks [Laurienti et al., Physica A 390 (20) (2011) 3608-3613.] and imply an existence of a scale-free density also within-among different self-similar scales of-complex real-world networks. The latter further improves the comprehension of self-similar structure in large real-world networks with several possible applications.