Vector Centrality in Hypergraphs

Abstract

Identifying the most influential nodes in networked systems is of vital importance to optimize their function and control. Several scalar metrics have been proposed to that effect, but the recent shift in focus towards network structures which go beyond a simple collection of dyadic interactions has rendered them void of performance guarantees. We here introduce a new measure of node's centrality, which is no longer a scalar value, but a vector with dimension one lower than the highest order of interaction in a hypergraph. Such a vectorial measure is linked to the eigenvector centrality for networks containing only dyadic interactions, but it has a significant added value in all other situations where interactions occur at higher-orders. In particular, it is able to unveil different roles which may be played by the same node at different orders of interactions -- information that is otherwise impossible to retrieve by single scalar measures. We demonstrate the efficacy of our measure with applications to synthetic networks and to three real world hypergraphs, and compare our results with those obtained by applying other scalar measures of centrality proposed in the literature.

10 pages, 9 figures

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Cited by in corpus (7)

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• Filtering higher-order datasets
• An application of node and edge nonlinear hypergraph centrality to a protein complex hypernetwork
• The relevance of higher-order ties
• Uplifting edges in higher order networks: spectral centralities for non-uniform hypergraphs
• Topological measures in weighted hypergraphs