Hyper-cores promote localization and efficient seeding in higher-order processes

Marco Mancastroppa, Iacopo Iacopini, Giovanni Petri and Alain Barrat ()
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Marco Mancastroppa: Aix Marseille Univ, Université de Toulon, CNRS, CPT, Turing Center for Living Systems
Iacopo Iacopini: Northeastern University London
Giovanni Petri: Northeastern University London
Alain Barrat: Aix Marseille Univ, Université de Toulon, CNRS, CPT, Turing Center for Living Systems

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Going beyond networks, to include higher-order interactions of arbitrary sizes, is a major step to better describe complex systems. In the resulting hypergraph representation, tools to identify structures and central nodes are scarce. We consider the decomposition of a hypergraph in hyper-cores, subsets of nodes connected by at least a certain number of hyperedges of at least a certain size. We show that this provides a fingerprint for data described by hypergraphs and suggests a novel notion of centrality, the hypercoreness. We assess the role of hyper-cores and nodes with large hypercoreness in higher-order dynamical processes: such nodes have large spreading power and spreading processes are localized in central hyper-cores. Additionally, in the emergence of social conventions very few committed individuals with high hypercoreness can rapidly overturn a majority convention. Our work opens multiple research avenues, from comparing empirical data to model validation and study of temporally varying hypergraphs.

Date: 2023
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DOI: 10.1038/s41467-023-41887-2

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