Strength distribution in gradient networks

TL;DR

This paper introduces a gradient network model where node weights depend on fitness differences, showing the strength distribution follows a power law with an exponent around 0.35, with implications for neuronal network topology.

Contribution

The paper presents an analytical and experimental study of a gradient network model with a novel weight assignment based on node fitness differences.

Findings

Strength distribution follows a power law with γ ≈ 0.35

Model has potential implications for neuronal networks

Analytical and experimental validation of the power law distribution

Abstract

This article describes a gradient complex network model whose weights are proportional to the difference between uniformly distributed ``fitness'' values assigned to the nodes. It is shown analytically and experimentally that the strength (i.e. the weighted node degree) density of such a network model can be well approximated by a power law with $\gamma \approx 0.35$. Possible implications for neuronal networks topology and dynamics are also discussed.