  The Artificial Benchmark for Community Detection (ABCD) graph is a recently
introduced random graph model with community structure and power-law
distribution for both degrees and community sizes. The model generates graphs
with similar properties as the well-known LFR one, and its main parameter can
be tuned to mimic its counterpart in the LFR model, the mixing parameter. In
this paper, we introduce hypergraph counterpart of the ABCD model, h-ABCD,
which produces random hypergraph with distributions of ground-truth community
sizes and degrees following power-law. As in the original ABCD, the new model
h-ABCD can produce hypergraphs with various levels of noise. More importantly,
the model is flexible and can mimic any desired level of homogeneity of
hyperedges that fall into one community. As a result, it can be used as a
suitable, synthetic playground for analyzing and tuning hypergraph community
detection algorithms.
