Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy
arXiv:2605.30432v1 Announce Type: cross Abstract: 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
The proliferation of complex network data, particularly from social interactions, is driving demand for advanced methods to extract actionable insights and predictive models.
Learning effective models from complex social network data is crucial for understanding societal dynamics and potentially for guiding interventions in critical areas, impacting sectors from public policy to marketing.
The application of Weak Form Sparse Identification of Nonlinear Dynamics (WSINDy) to social systems may enhance our ability to build data-driven predictive models for complex human interactions, leading to more robust forecasting.
- · AI/ML research institutions
- · Social scientists
- · Data scientists
- · Analytics software providers
- · Traditional statistical modeling approaches for social systems
- · Organizations relying solely on qualitative social analysis
Improved understanding and predictive capability for social and behavioral patterns on online and offline networks.
Development of more sophisticated AI agents capable of navigating and influencing complex social environments based on learned dynamics.
Ethical and societal debates around the use of highly predictive models of human social behavior for control or manipulation.
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