000151234 001__ 151234
000151234 005__ 20251017144651.0
000151234 0247_ $$2doi$$a10.1098/rspa.2024.0435
000151234 0248_ $$2sideral$$a143030
000151234 037__ $$aART-2025-143030
000151234 041__ $$aeng
000151234 100__ $$aRodrigues, Francisco A.
000151234 245__ $$aA machine learning approach to predicting dynamical observables from network structure
000151234 260__ $$c2025
000151234 5060_ $$aAccess copy available to the general public$$fUnrestricted
000151234 5203_ $$aEstimating the outcome of a given dynamical process from structural features is a key unsolved challenge in network science. This goal is hampered by difficulties associated with nonlinearities, correlations and feedbacks between the structure and dynamics of complex systems. In this work, we develop an approach based on machine learning algorithms that provides an important step towards understanding the relationship between the structure and dynamics of networks. In particular, it allows us to estimate from the network structure the outbreak size of a disease starting from a single node, as well as the degree of synchronicity of a system made up of Kuramoto oscillators. We show which topological features of the network are key for this estimation and provide a ranking of the importance of network metrics with much higher accuracy than previously done. For epidemic propagation, the k-core plays a fundamental role, while for synchronization, the betweenness centrality and accessibility are the measures most related to the state of an oscillator. For all the networks, we find that random forests can predict the outbreak size or synchronization state with high accuracy, indicating that the network structure plays a fundamental role in the spreading process. Our approach is general and can be applied to almost any dynamic process running on complex networks. Also, our work is an important step towards applying machine learning methods to unravel dynamical patterns that emerge in complex networked systems.
000151234 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E36-23R-FENOL$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-115800GB-I00
000151234 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000151234 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000151234 700__ $$aPeron, Thomas
000151234 700__ $$aConnaughton, Colm
000151234 700__ $$aKurths, Jürgen
000151234 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Yamir$$uUniversidad de Zaragoza
000151234 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000151234 773__ $$g481, 2306 (2025), [12 pp.]$$pProc. - Royal Soc., Math. phys. eng. sci.$$tProceedings - Royal Society. Mathematical, physical and engineering sciences$$x1364-5021
000151234 8564_ $$s5778949$$uhttps://zaguan.unizar.es/record/151234/files/texto_completo.pdf$$yVersión publicada
000151234 8564_ $$s1839032$$uhttps://zaguan.unizar.es/record/151234/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000151234 909CO $$ooai:zaguan.unizar.es:151234$$particulos$$pdriver
000151234 951__ $$a2025-10-17-14:36:36
000151234 980__ $$aARTICLE