Introduces SIGN, a framework capable of discovering governing symbolic equations for networked systems with over 100,000 nodes.
April 2, 2026
Original Paper
Predicting Dynamics of Ultra-Large Complex Systems by Inferring Governing Equations
arXiv · 2604.00599
The Takeaway
It solves the interpretability-scalability trade-off in complex systems by decoupling equation discovery from network size. This allows researchers to extract physical laws from massive datasets like global sea surface temperatures or neural dynamics where standard symbolic regression fails.
From the 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 govern