The probabilistic backbone of data-driven complex networks: an example in climate.

ABSTRACT: Complex systems often exhibit long-range correlations so that typical observables show statistical dependence across long distances. These teleconnections have a tremendous impact on the dynamics as they provide channels for information transport across the system and are particularly relevant in forecasting, control, and data-driven modeling of complex systems. These statistical interrelations among the very many degrees of freedom are usually represented by the so-called correlation network, constructed by establishing links between variables (nodes) with pairwise correlations above a given threshold. Here, with the climate system as an example, we revisit correlation networks from a probabilistic perspective and show that they unavoidably include much redundant information, resulting in

SUBMITTER: Graafland CE 

PROVIDER: S-EPMC7359351 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature