Conference Papers Year : 2020

A Random Growth Model with any Real or Theoretical Degree Distribution

Abstract

The degree distributions of complex networks are usually considered to be power law. However, it is not the case for a large number of them. We thus propose a new model able to build random growing networks with (almost) any wanted degree distribution. The degree distribution can either be theoretical or extracted from a real-world network. The main idea is to invert the recurrence equation commonly used to compute the degree distribution in order to find a convenient attachment function for node connections-commonly chosen as linear. We compute this attachment function for some classical distributions, as the power-law, broken power-law, geometric and Poisson distributions. We also use the model on an undirected version of the Twitter network, for which the degree distribution has an unusual shape.
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Dates and versions

hal-03052144 , version 1 (10-12-2020)

Identifiers

  • HAL Id : hal-03052144 , version 1

Cite

Frédéric Giroire, Stéphane Pérennes, Thibaud Trolliet. A Random Growth Model with any Real or Theoretical Degree Distribution. COMPLEX NETWORKS 2020 - 9th International Conference on Complex Networks and their Applications, Dec 2020, Madrid / Virtual, Spain. ⟨hal-03052144⟩
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