  We introduce a random hypergraph model for core-periphery structure. By
leveraging our model's sufficient statistics, we develop a novel statistical
inference algorithm that is able to scale to large hypergraphs with runtime
that is practically linear wrt. the number of nodes in the graph after a
preprocessing step that is almost linear in the number of hyperedges, as well
as a scalable sampling algorithm. Our inference algorithm is capable of
learning embeddings that correspond to the reputation (rank) of a node within
the hypergraph. We also give theoretical bounds on the size of the core of
hypergraphs generated by our model. We experiment with hypergraph data that
range to $\sim 10^5$ hyperedges mined from the Microsoft Academic Graph, Stack
Exchange, and GitHub and show that our model outperforms baselines wrt.
producing good fits.
