Gaskell, J. , Campioni, N., Morales, J. M. , Husmeier, D. and Torney, C. J. (2023) Inferring the interaction rules of complex systems with graph neural networks and approximate Bayesian computation. Journal of the Royal Society: Interface, 20(198), 20220676. (doi: 10.1098/rsif.2022.0676) (PMID:36596456)
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Abstract
Inferring the underlying processes that drive collective behaviour in biological and social systems is a significant statistical and computational challenge. While simulation models have been successful in qualitatively capturing many of the phenomena observed in these systems in a variety of domains, formally fitting these models to data remains intractable. Recently, approximate Bayesian computation (ABC) has been shown to be an effective approach to inference if the likelihood function for a model is unavailable. However, a key difficulty in successfully implementing ABC lies with the design, selection and weighting of appropriate summary statistics, a challenge that is especially acute when modelling high dimensional complex systems. In this work, we combine a Gaussian process accelerated ABC method with the automatic learning of summary statistics via graph neural networks. Our approach bypasses the need to design a model-specific set of summary statistics for inference. Instead, we encode relational inductive biases into a neural network using a graph embedding and then extract summary statistics automatically from simulation data. To evaluate our framework, we use a model of collective animal movement as a test bed and compare our method to a standard summary statistics approach and a linear regression-based algorithm.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Gaskell, Dr Jennifer and Morales, Professor Juan and Husmeier, Professor Dirk and Campioni, Nazareno and Torney, Professor Colin |
Creator Roles: | Gaskell, J.Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review and editing Campioni, N.Conceptualization, Visualization Morales, J. M.Conceptualization, Funding acquisition, Investigation, Supervision, Writing – review and editing Husmeier, D.Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – review and editing Torney, C. J.Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review and editing |
Authors: | Gaskell, J., Campioni, N., Morales, J. M., Husmeier, D., and Torney, C. J. |
College/School: | College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine College of Science and Engineering > School of Mathematics and Statistics > Mathematics College of Science and Engineering > School of Mathematics and Statistics > Statistics |
Journal Name: | Journal of the Royal Society: Interface |
Publisher: | The Royal Society |
ISSN: | 1742-5689 |
ISSN (Online): | 1742-5662 |
Published Online: | 04 January 2023 |
Copyright Holders: | Copyright © 2023 The Authors |
First Published: | First published in Journal of the Royal Society: Interface 20(198): 20220676 |
Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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