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Preferential attachment hypergraph with high modularity
- Source :
- Network Science, Network Science, 2022, 10 (4), pp.400-429. ⟨10.1017/nws.2022.35⟩
- Publication Year :
- 2022
- Publisher :
- Cambridge University Press (CUP), 2022.
-
Abstract
- 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 also, just a few models allow 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 research domains in order to reflect better real-life phenomena.
- Subjects :
- Social and Information Networks (cs.SI)
FOS: Computer and information sciences
Physics - Physics and Society
preferential attachment
05C82, 05C80
Sociology and Political Science
Social Psychology
Communication
hypergraph
FOS: Physical sciences
Computer Science - Social and Information Networks
Physics and Society (physics.soc-ph)
complex network
FOS: Mathematics
Mathematics - Combinatorics
[INFO]Computer Science [cs]
Combinatorics (math.CO)
[MATH]Mathematics [math]
modularity
Subjects
Details
- ISSN :
- 20501250 and 20501242
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- Network Science
- Accession number :
- edsair.doi.dedup.....b91784c1a39065ac6ef0d7ef5df57e85