Inferring community structure in attributed hypergraphs using stochastic block models

Abstract Hypergraphs represent complex systems involving interactions among more than two entities and allow the investigation of higher-order structure and dynamics in real-world complex systems. Stochastic block models have been employed to investigate community structure in networks. Node attribute data, often accompanying network data, has been found to potentially enhance the learning of community structure in dyadic networks. In this study, we develop a statistical framework for incorporating node attribute data into the inference of community structure in a hypergraph, employing a stochastic block model. We demonstrate that our model, referred to as HyperNEO, enhances the inference of community structure in hypergraphs when node attributes are sufficiently associated with the communities. We also showcase the use of stochastic block models, including our model, for projecting the nodes in a hypergraph into a two-dimensional vector space. We found that this projection simplifies the identification of a set of nodes with similar community memberships in empirical hypergraphs. We expect our framework to broaden the investigation and understanding of higher-order community structure in complex systems.