[2604.00599] Predicting Dynamics of Ultra-Large Complex Systems by Inferring Governing Equations

Computer Science > Machine Learning arXiv:2604.00599 (cs) [Submitted on 1 Apr 2026] Title:Predicting Dynamics of Ultra-Large Complex Systems by Inferring Governing Equations Authors:Qi Shao, Duxin Chen, Jiawen Chen, Yujie Zeng, Athen Ma, Wenwu Yu, Vito Latora, Wei Lin View a PDF of the paper titled Predicting Dynamics of Ultra-Large Complex Systems by Inferring Governing Equations, by Qi Shao and 7 other authors View PDF HTML (experimental) Abstract: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 susta...