Canonical fitness model for simple scale-free graphs

TL;DR

This paper analyzes a fitness model for generating simple scale-free graphs with a power-law degree distribution, revealing a universal tail behavior and providing analytical and numerical insights into its properties.

Contribution

It introduces a detailed analysis of the fitness model for simple graphs, showing the asymptotic degree distribution and the relation between fitness and expected degree.

Findings

The model produces a degree distribution with a k^{-2} tail for large degrees.

Analytical results are confirmed by numerical simulations.

The model's behavior is characterized for different regimes of the fitness parameter.

Abstract

We consider a fitness model assumed to generate simple graphs with power-law heavy-tailed degree sequence: P(k) \propto k^{-1-\alpha} with 0 < \alpha < 1, in which the corresponding distributions do not posses a mean. We discuss the situations in which the model is used to produce a multigraph and examine what happens if the multiple edges are merged to a single one and thus a simple graph is built. We give the relation between the (normalized) fitness parameter r and the expected degree \nu of a node and show analytically that it possesses non-trivial intermediate and final asymptotic behaviors. We show that the model produces P(k) \propto k^{-2} for large values of k independent of \alpha. Our analytical findings are confirmed by numerical simulations.