Search papers, labs, and topics across Lattice.
The paper introduces Sparse Identification Graph Neural Network (SIGN), a framework for inferring governing equations of ultra-large networked systems from data. SIGN decouples the scalability of sparse identification from network size by defining symbolic discovery as edge-level information, enabling efficient equation discovery in systems with over 100,000 nodes. The method is validated on diverse benchmarks and applied to sea surface temperature data, achieving accurate long-term predictions and identifying a compact predictive network model.
Unlock interpretable and reliable predictions for ultra-large complex systems, like climate patterns, by inferring governing equations at scales previously inaccessible.
Predicting the behavior of ultra-large complex systems, from climate to biological and technological networks, is a central unsolved challenge. Existing approaches face a fundamental trade-off: equation discovery methods provide interpretability but fail to scale, while neural networks scale but operate as black boxes and often lose reliability over long times. Here, we introduce the Sparse Identification Graph Neural Network, a framework that overcome this divide by allowing to infer the governing equations of large networked systems from data. By defining symbolic discovery as edge-level information, SIGN decouples the scalability of sparse identification from network size, enabling efficient equation discovery even in large systems. SIGN allows to study networks with over 100,000 nodes while remaining robust to noise, sparse sampling, and missing data. Across diverse benchmark systems, including coupled chaotic oscillators, neural dynamics, and epidemic spreading, it recovers governing equations with high precision and sustains accurate long-term predictions. Applied to a data set of time series of temperature measurements in 71,987 sea surface positions, SIGN identifies a compact predictive network model and captures large-scale sea surface temperature conditions up to two years in advance. By enabling equation discovery at previously inaccessible scales, SIGN opens a path toward interpretable and reliable prediction of real-world complex systems.
