| Full text | |
| Author(s): |
Rodrigues, Francisco A.
;
Peron, Thomas
;
Connaughton, Colm
;
Kurths, Jurgen
;
Moreno, Yamir
Total Authors: 5
|
| Document type: | Journal article |
| Source: | PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SC; v. 481, n. 2306, p. 12-pg., 2025-01-29. |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 24/02322-0 - Predictive modeling of complex network structure and dynamics using machine learning methods |
| Grantee: | Francisco Aparecido Rodrigues |
| Support Opportunities: | Research Grants - Visiting Researcher Grant - International |
| FAPESP's process: | 15/50122-0 - Dynamic phenomena in complex networks: basics and applications |
| Grantee: | Elbert Einstein Nehrer Macau |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 20/09835-1 - IARA - Artificial Intelligence in the Remaking of Urban Environments |
| Grantee: | André Carlos Ponce de Leon Ferreira de Carvalho |
| Support Opportunities: | Research Grants - Research Centers in Engineering Program |
| FAPESP's process: | 23/07481-6 - Synchronization in networks: applications in neuroscience |
| Grantee: | Thomas Kaue Dal Maso Peron |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |