Higher-order correlations reveal complex memory in temporal hypergraphs.
Nat Commun
; 15(1): 4754, 2024 Jun 04.
Article
de En
| MEDLINE
| ID: mdl-38834592
ABSTRACT
Many real-world complex systems are characterized by interactions in groups that change in time. Current temporal network approaches, however, are unable to describe group dynamics, as they are based on pairwise interactions only. Here, we use time-varying hypergraphs to describe such systems, and we introduce a framework based on higher-order correlations to characterize their temporal organization. The analysis of human interaction data reveals the existence of coherent and interdependent mesoscopic structures, thus capturing aggregation, fragmentation and nucleation processes in social systems. We introduce a model of temporal hypergraphs with non-Markovian group interactions, which reveals complex memory as a fundamental mechanism underlying the emerging pattern in the data.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Langue:
En
Journal:
Nat Commun
Sujet du journal:
BIOLOGIA
/
CIENCIA
Année:
2024
Type de document:
Article
Pays d'affiliation:
Autriche
Pays de publication:
ENGLAND
/
ESCOCIA
/
GB
/
GREAT BRITAIN
/
INGLATERRA
/
REINO UNIDO
/
SCOTLAND
/
UK
/
UNITED KINGDOM