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Inferring the interaction rules of complex systems with graph neural networks and approximate Bayesian computation
Jennifer Gaskell1, Nazareno Campioni1, Juan M Morales2,3
1School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8SQ, UK.
Journal of the Royal Society, Interface
|January 3, 2023
Summary
This study introduces a novel method for analyzing complex systems by automatically learning summary statistics using graph neural networks. This approach simplifies the inference process for simulation models, particularly in collective behavior research.
Area of Science:
- Computational Biology
- Statistical Modeling
- Complex Systems
Background:
- Inferring processes in collective behavior is challenging.
- Simulation models capture phenomena but are hard to fit to data.
- Approximate Bayesian computation (ABC) is useful when likelihood is unavailable.
Purpose of the Study:
- To develop a method for automatic summary statistics learning in ABC.
- To bypass the need for manual summary statistics design in complex systems.
- To improve the tractability of fitting simulation models to data.
Main Methods:
- Combined Gaussian process accelerated ABC with graph neural networks.
- Used graph embeddings to encode relational inductive biases.
- Automatically extracted summary statistics from simulation data.
Main Results:
- The framework bypasses the need for model-specific summary statistics.
- Demonstrated effectiveness using a collective animal movement model.
- Outperformed standard summary statistics and linear regression approaches.
Conclusions:
- Automatic summary statistics learning via graph neural networks is effective for ABC.
- This method enhances the analysis of high-dimensional complex systems.
- Offers a more tractable approach to fitting simulation models to empirical data.
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