Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Zeitschriftenartikel

Topology shapes dynamics of higher-order networks

Urheber*innen

Millán,  Ana P.
External Organizations;

Sun,  Hanlin
External Organizations;

Giambagli,  Lorenzo
External Organizations;

Muolo,  Riccardo
External Organizations;

Carletti,  Timoteo
External Organizations;

Torres,  Joaquín J.
External Organizations;

Radicchi,  Filippo
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Bianconi,  Ginestra
External Organizations;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PIKpublic verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Millán, A. P., Sun, H., Giambagli, L., Muolo, R., Carletti, T., Torres, J. J., Radicchi, F., Kurths, J., Bianconi, G. (2025 online): Topology shapes dynamics of higher-order networks. - Nature Physics.
https://doi.org/10.1038/s41567-024-02757-w


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_31927
Zusammenfassung
Higher-order networks capture the many-body interactions present in complex systems, shedding light on the interplay between topology and dynamics. The theory of higher-order topological dynamics, which combines higher-order interactions with discrete topology and nonlinear dynamics, has the potential to enhance our understanding of complex systems, such as the brain and the climate, and to advance the development of next-generation AI algorithms. This theoretical framework, which goes beyond traditional node-centric descriptions, encodes the dynamics of a network through topological signals—variables assigned not only to nodes but also to edges, triangles and other higher-order cells. Recent findings show that topological signals lead to the emergence of distinct types of dynamical state and collective phenomena, including topological and Dirac synchronization, pattern formation and triadic percolation. These results offer insights into how topology shapes dynamics, how dynamics learns topology and how topology evolves dynamically. This Perspective primarily aims to guide physicists, mathematicians, computer scientists and network scientists through the emerging field of higher-order topological dynamics, while also outlining future research challenges.