Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy
Social systems consist of networks of individuals who influence one another through social interactions. Studying how processes evolve on these networks can help us better understand patterns of social behavior. We study a system that couples online and offline social activity and investigate how to learn effective models directly from data using Weak Form Sparse Identification of Nonlinear Dynamics (WSINDy), a method for discovering governing equations. We assess learning performance using data generated by a mean-field approximation model of a stochastic interaction process on networks and test how accurately the system can be recovered under different noise levels. Our results show tha...
| Authors | Moyi Tian, Daniel A. Messenger, Vanja Dukic et al. |
|---|---|
| Primary category | math.DS |
| Published | 28 May 2026 |
| Last updated at source | 5 June 2026 |
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