Higher-order correlations reveal complex memory in temporal hypergraphs
Luca Gallo (),
Lucas Lacasa,
Vito Latora and
Federico Battiston ()
Additional contact information
Luca Gallo: Central European University
Lucas Lacasa: CSIC-UIB
Vito Latora: Queen Mary University of London
Federico Battiston: Central European University
Nature Communications, 2024, vol. 15, issue 1, 1-7
Abstract:
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.
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48578-6
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DOI: 10.1038/s41467-024-48578-6
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