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A Random Growth Model with any Real or Theoretical Degree Distribution

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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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