A family of models of growing hypergraphs with preferential rules of new linking is introduced and studied. The model hypergraphs evolve via the hyperedge-based growth as well as the node-based one, thus generalizing the preferential attachment models of scale-free networks. We obtain the degree distribution and hyperedge size distribution for various combinations of node- and hyperedge-based growth modes. We find that the introduction of hyperedge-based growth can give rise to power law degree distribution P(k) ∼ k-γ even without node-wise preferential attachments. The hyperedge size distribution P(s) can take diverse functional forms, ranging from exponential to power law to a nonstationary one, depending on the specific hyperedge-based growth rule. Numerical simulations support the mean-field theoretical analytical predictions.
Bibliographical notePublisher Copyright:
© 2023, The Korean Physical Society.
- Growing hypergraph
- Power law distribution
- Preferential linking
ASJC Scopus subject areas
- General Physics and Astronomy