Scale invariance and statistical significance in complex weighted networks

TL;DR

This paper investigates the scale dependence of statistical significance in weighted networks, revealing that traditional null models like the WCM are affected by scale choices, and proposes a two-step method to achieve scale-invariant significance testing.

Contribution

It introduces a novel two-step approach that restores scale invariance in null models for weighted networks, enabling unbiased significance assessments.

Findings

WCM's significance results depend on weight scale.

Scale invariance is crucial for unbiased network analysis.

A two-step method restores scale invariance in weighted network null models.

Abstract

Most networks encountered in nature, society, and technology have weighted edges, representing the strength of the interaction/association between their vertices. Randomizing the structure of a network is a classic procedure used to estimate the statistical significance of properties of the network, such as transitivity, centrality and community structure. Randomization of weighted networks has traditionally been done via the weighted configuration model (WCM), a simple extension of the configuration model, where weights are interpreted as bundles of edges. It has previously been shown that the ensemble of randomizations provided by the WCM is affected by the specific scale used to compute the weights, but the consequences for statistical significance were unclear. Here we find that statistical significance based on the WCM is scale-dependent, whereas in most cases results should be independent of the choice of the scale. More generally, we find that designing a null model that does not violate scale invariance is challenging. A two-step approach, originally introduced for network reconstruction, in which one first randomizes the structure, then the weights, with a suitable distribution, restores scale invariance, and allows us to conduct unbiased assessments of significance on weighted networks.