dc.contributor.author | Caligiuri, A. | |
dc.contributor.author | Eguíluz, V.M. | |
dc.contributor.author | Di Gaetano, L. | |
dc.contributor.author | Galla, T. | |
dc.contributor.author | Lacasa, L. | |
dc.date.accessioned | 2023-06-14T11:21:00Z | |
dc.date.available | 2023-06-14T11:21:00Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Physical Review E: 107 (4): 44305 (2023) | es_ES |
dc.identifier.uri | http://hdl.handle.net/10810/61376 | |
dc.description.abstract | By interpreting a temporal network as a trajectory of a latent graph dynamical system, we introduce the concept of dynamical instability of a temporal network and construct a measure to estimate the network maximum Lyapunov exponent (nMLE) of a temporal network trajectory. Extending conventional algorithmic methods from nonlinear time-series analysis to networks, we show how to quantify sensitive dependence on initial conditions and estimate the nMLE directly from a single network trajectory. We validate our method for a range of synthetic generative network models displaying low- and high-dimensional chaos and finally discuss potential applications. | es_ES |
dc.description.sponsorship | We thank Federico Battiston for helpful comments on initial phases of this research, and Emilio Hernández-Fernández, Sandro Meloni, Lluis Arola-Fernández, Ernesto Estrada, Massimiliano Zanin, Diego Pazó, and Juan Manuel López for helpful discussions around several aspects of the work. A.C. acknowledges funding by the Maria de Maeztu Programme (MDM-2017-0711) and the AEI under the FPI programme. T.G. acknowledges support from the AEI and Fondo Europeo de Desarrollo Regional (FEDER, UE) under project APASOS (PID2021-122256NB-C21/PID2021-122256NB-C22). L.L. acknowledges funding from project DYNDEEP (No. EUR2021-122007), and L.L. and V.M.E. acknowledge funding from project MISLAND (No. PID2020-114324GB-C22), both projects funded by MCIN/AEI/10.13039/501100011033. This work has been partially supported by the María de Maeztu Project No. CEX2021-001164-M funded by MCIN/AEI/10.13039/501100011033. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Physical Review E | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/MDM-2017-0714 | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/EUR2021-122007 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/PID2020-114324GB-C22 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MCIU/CEX2021-001164-M | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ | * |
dc.subject | Lyapunov exponent | es_ES |
dc.subject | temporal networks | es_ES |
dc.subject | chaos | es_ES |
dc.subject | complex systems | es_ES |
dc.title | Lyapunov exponents for temporal networks | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | ©2023 American Physical Society | es_ES |
dc.rights.holder | Atribución-NoComercial-CompartirIgual 3.0 España | * |
dc.relation.publisherversion | https://dx.doi.org/10.1103/PhysRevE.107.044305 | es_ES |
dc.identifier.doi | 10.1103/PhysRevE.107.044305 | |