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Medientyp:
E-Artikel
Titel:
Growing scale-free simplices
Beteiligte:
Kovalenko, Kiriil;
Sendiña-Nadal, Irene;
Khalil, Nagi;
Dainiak, Alex;
Musatov, Daniil;
Raigorodskii, Andrei M.;
Alfaro-Bittner, Karin;
Barzel, Baruch;
Boccaletti, Stefano
Erschienen:
Springer Science and Business Media LLC, 2021
Erschienen in:Communications Physics
Sprache:
Englisch
DOI:
10.1038/s42005-021-00538-y
ISSN:
2399-3650
Entstehung:
Anmerkungen:
Beschreibung:
<jats:title>Abstract</jats:title><jats:p>The past two decades have seen significant successes in our understanding of networked systems, from the mapping of real-world networks to the establishment of generative models recovering their observed macroscopic patterns. These advances, however, are restricted to pairwise interactions and provide limited insight into higher-order structures. Such multi-component interactions can only be grasped through simplicial complexes, which have recently found applications in social, technological, and biological contexts. Here we introduce a model to grow simplicial complexes of order two, i.e., nodes, links, and triangles, that can be straightforwardly extended to structures containing hyperedges of larger order. Specifically, through a combination of preferential and/or nonpreferential attachment mechanisms, the model constructs networks with a scale-free degree distribution and an either bounded or scale-free generalized degree distribution. We arrive at a highly general scheme with analytical control of the scaling exponents to construct ensembles of synthetic complexes displaying desired statistical properties.</jats:p>