Graph Neural Ordinary Differential Equations for Coarse-Grained Socioeconomic Dynamics
<|reference_start|>Graph Neural Ordinary Differential Equations for Coarse-Grained Socioeconomic Dynamics: We present a data-driven machine-learning approach for modeling space-time socioeconomic dynamics. Through coarse-graining fine-scale observations, our modeling framework simplifies these complex systems to a set of tractable mechanistic relationships -- in the form of ordinary differential equations -- while preserving critical system behaviors. This approach allows for expedited 'what if' studies and sensitivity analyses, essential for informed policy-making. Our findings, from a case study of Baltimore, MD, indicate that this machine learning-augmented coarse-grained model serves as a powerful instrument for deciphering the complex interactions between social factors, geography, and exogenous stressors, offering a valuable asset for system forecasting and resilience planning.<|reference_end|>
@article{koch2024graph,
    title={Graph Neural Ordinary Differential Equations for Coarse-Grained
  Socioeconomic Dynamics},
    author={James Koch, Pranab Roy Chowdhury, Heng Wan, Parin Bhaduri, Jim Yoon,
  Vivek Srikrishnan, W. Brent Daniel},
    journal={arXiv preprint arXiv:2407.18108},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18108},
    primaryClass={cs.LG cs.CY cs.SI physics.soc-ph}
}