Uplifting edges in higher order networks: spectral centralities for non-uniform hypergraphs

Abstract

Spectral analysis of networks states that many structural properties of graphs, such as centrality of their nodes, are given in terms of their adjacency matrices. The natural extension of such spectral analysis to higher order networks is strongly limited by the fact that a given hypergraph could have several different adjacency hypermatrices, hence the results obtained so far are mainly restricted to the class of uniform hypergraphs, which leaves many real systems unattended. A new method for analysing non-linear eigenvector-like centrality measures of non-uniform hypergraphs is presented in this paper that could be useful for studying properties of \(\mathcal{H}\)-eigenvectors and \(\mathcal{Z}\)-eigenvectors in the non-uniform case. In order to do so, a new operation - the \(\textit{uplift}\) - is introduced, incorporating auxiliary nodes in the hypergraph to allow for a uniform-like analysis. We later argue why this is a mathematically sound operation, and we furthermore use it to classify a whole family of hypergraphs with unique Perron-like \(\mathcal{Z}\)-eigenvectors. We supplement the theoretical analysis with several examples and numerical simulations on synthetic and real datasets.