Growing Hypergraphs with Preferential Linking

Abstract: 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 $P(k)\sim k^{-\gamma}$ 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.