Parameterizing network graph heterogeneity using a modified Weibull distribution

Abstract We present a simple method to quantitatively capture the heterogeneity in the degree distribution of a network graph using a single parameter $$\sigma$$ σ . Using an exponential transformation of the shape parameter of the Weibull distribution, this control parameter allows the degree distr...

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Bibliographic Details
Main Authors: Sinan A. Ozbay, Maximilian M. Nguyen
Format: Article
Language:English
Published: SpringerOpen 2023-04-01
Series:Applied Network Science
Online Access:https://doi.org/10.1007/s41109-023-00544-9
Description
Summary:Abstract We present a simple method to quantitatively capture the heterogeneity in the degree distribution of a network graph using a single parameter $$\sigma$$ σ . Using an exponential transformation of the shape parameter of the Weibull distribution, this control parameter allows the degree distribution to be easily interpolated between highly symmetric and highly heterogeneous distributions on the unit interval. This parameterization of heterogeneity also recovers several other canonical distributions as intermediate special cases, including the Gaussian, Rayleigh, and exponential distributions. We then outline a general graph generation algorithm to produce graphs with a desired amount of heterogeneity. The utility of this formulation of a heterogeneity parameter is demonstrated with examples relating to epidemiological modeling and spectral analysis.
ISSN:2364-8228