Random Connection Hypergraphs

TL;DR

This paper introduces a new random hypergraph model based on weighted connection processes, analyzing its topological properties and distributional limits, with applications to real-world collaboration networks.

Contribution

It presents a generalized hypergraph model combining weighted connection and geometric features, and studies its asymptotic topological and distributional properties.

Findings

Higher-order degree distributions characterized

Betti numbers of the Dowker complex analyzed

Distributional limits depend on weight heaviness

Abstract

In this paper, we introduce a novel model for random hypergraphs based on weighted random connection models. In accordance with the standard theory for hypergraphs, this model is constructed from a bipartite graph. In our stochastic model, both vertex sets of this bipartite graph form marked Poisson point processes, and the connection radius is inversely proportional to a product of suitable powers of the marks. Hence, our model is a common generalization of weighted random connection models and AB random geometric graphs. For this hypergraph model, we investigate the limit theory of various graph-theoretic and topological characteristics, including higher-order degree distributions, Betti numbers of the associated Dowker complex, and simplex counts. In particular, for the latter quantity, we identify regimes of convergence to normal and to stable distribution depending on the heavy-tailedness of the weight distribution. We conclude our investigation with a simulation study and an application to the collaboration network extracted from the arXiv dataset.