Preferential attachment hypergraph with high modularity - Archive ouverte HAL Access content directly
Reports (Research Report) Year : 2021

Preferential attachment hypergraph with high modularity


Numerous works have been proposed to generate random graphs preserving the same properties as real-life large scale networks. However, many real networks are better represented by hypergraphs. Few models for generating random hypergraphs exist and no general model allows to both preserve a power-law degree distribution and a high modularity indicating the presence of communities. We present a dynamic preferential attachment hypergraph model which features partition into communities. We prove that its degree distribution follows a power-law and we give theoretical lower bounds for its modularity. We compare its characteristics with a real-life co-authorship network and show that our model achieves good performances. We believe that our hypergraph model will be an interesting tool that may be used in many reasearch domains in order to reflect better real-life phenomena.
Fichier principal
Vignette du fichier
Projet_SNIF___Hypergraph_Model_with_Modularity(2).pdf (460.98 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03154836 , version 1 (01-03-2021)


  • HAL Id : hal-03154836 , version 1


Frédéric Giroire, Nicolas Nisse, Thibaud Trolliet, Malgorzata Sulkowska. Preferential attachment hypergraph with high modularity. [Research Report] Université Cote d'Azur. 2021. ⟨hal-03154836⟩
82 View
163 Download


Gmail Facebook Twitter LinkedIn More