  We consider the community recovery problem on a multilayer variant of the
hypergraph stochastic block model (HSBM). Each layer is associated with an
independent realization of a d-uniform HSBM on N vertices. Given the similarity
matrix containing the aggregated number of hyperedges incident to each pair of
vertices, the goal is to obtain a partition of the N vertices into disjoint
communities. In this work, we investigate a semidefinite programming (SDP)
approach and obtain information-theoretic conditions on the model parameters
that guarantee exact recovery both in the assortative and the disassortative
cases.
