Higher-order correlations reveal complex memory in temporal hypergraphs

Luca GalloLucas LacasaVito LatoraFederico Battiston

Mar 2023

摘要

Many real-world complex systems are characterized by interactions in groupsthat change in time. Current temporal network approaches, however, are unableto describe group dynamics, as they are based on pairwise interactions only.Here, we use time-varying hypergraphs to describe such systems, and weintroduce a framework based on higher-order correlations to characterize theirtemporal organization. We analyze various social systems, finding that groupsof different sizes have typical patterns of long-range temporal correlations.Moreover, our method reveals the presence of non-trivial temporalinterdependencies between different group sizes. We introduce a model oftemporal hypergraphs with non-Markovian group interactions, which revealscomplex memory as a fundamental mechanism underlying the pattern in the data.

展开全部

图表提取

论文十问

参考文献

被引用

提问与回答

暂无人提供速读十问回答

被引用

笔记

问答

领域