  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 research
domains in order to reflect better real-life phenomena.
