Latent Space Modelling of Hypergraph Data

Understand

The increasing prevalence of relational data describing interactions among a target population has motivated a wide literature on statistical network analysis.

• In many applications, interactions may involve more than two members of the population and this data is more appropriately represented by a hypergraph.
• In this paper, we present a model for hypergraph data which extends the well established latent space approach for graphs and, by drawing a connection to constructs from computational topology, we develop a model whose likelihood is inexpensive to compute.
• A delayed-acceptance MCMC scheme is proposed to obtain posterior samples and we rely on Bookstein coordinates to remove the identifiability issues associated with the latent representation.