  We propose a model to address the overlooked problem of node clustering in
simple hypergraphs. Simple hypergraphs are suitable when a node may not appear
multiple times in the same hyperedge, such as in co-authorship datasets. Our
model generalizes the stochastic blockmodel for graphs and assumes the
existence of latent node groups and hyperedges are conditionally independent
given these groups. We first establish the generic identifiability of the model
parameters. We then develop a variational approximation
Expectation-Maximization algorithm for parameter inference and node clustering,
and derive a statistical criterion for model selection.
  To illustrate the performance of our R package HyperSBM, we compare it with
other node clustering methods using synthetic data generated from the model, as
well as from a line clustering experiment and a co-authorship dataset.
