# True scale-free networks hidden by finite size effects

## TL;DR

This study investigates whether many real-world networks are truly scale-free or if finite-size effects obscure their scale invariance, finding that many networks do exhibit scale-free properties once finite-size effects are accounted for.

## Contribution

The paper demonstrates that many real networks are inherently scale-free, with finite-size effects often hiding this property, challenging previous assumptions based solely on degree distribution analysis.

## Key findings

- Many networks follow finite size scaling without self-tuning.
- Biological, technological, and informational networks often exhibit true scale invariance.
- Infrastructure and social networks show deviations from scale-free behavior.

## Abstract

We analyze about two hundred naturally occurring networks with distinct dynamical origins to formally test whether the commonly assumed hypothesis of an underlying scale-free structure is generally viable. This has recently been questioned on the basis of statistical testing of the validity of power law distributions of network degrees by contrasting real data. Specifically, we analyze by finite-size scaling analysis the datasets of real networks to check whether purported departures from the power law behavior are due to the finiteness of the sample size. In this case, power laws would be recovered in the case of progressively larger cutoffs induced by the size of the sample. We find that a large number of the networks studied follow a finite size scaling hypothesis without any self-tuning. This is the case of biological protein interaction networks, technological computer and hyperlink networks, and informational networks in general. Marked deviations appear in other cases, especially infrastructure and transportation but also social networks. We conclude that underlying scale invariance properties of many naturally occurring networks are extant features often clouded by finite-size effects due to the nature of the sample data.