151234 20251017144651.0 doi 10.1098/rspa.2024.0435 sideral 143030 ART-2025-143030 eng Rodrigues, Francisco A. A machine learning approach to predicting dynamical observables from network structure 2025 Access copy available to the general public Unrestricted Estimating 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. info:eu-repo/grantAgreement/ES/DGA/E36-23R-FENOL info:eu-repo/grantAgreement/ES/MICINN/PID2020-115800GB-I00 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peron, Thomas Connaughton, Colm Kurths, Jürgen Moreno, Yamir Universidad de Zaragoza (orcid)0000-0002-0895-1893 2004 405 Universidad de Zaragoza Dpto. Física Teórica Área Física Teórica 481, 2306 (2025), [12 pp.] Proc. - Royal Soc., Math. phys. eng. sci. Proceedings - Royal Society. Mathematical, physical and engineering sciences 1364-5021 5778949 http://zaguan.unizar.es/record/151234/files/texto_completo.pdf Versión publicada 1839032 http://zaguan.unizar.es/record/151234/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:151234 articulos driver 2025-10-17-14:36:36 ARTICLE