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dc.contributor.author
Gaskell, Jennifer
dc.contributor.author
Campioni, Nazareno
dc.contributor.author
Morales, Juan Manuel

dc.contributor.author
Husmeier, Dirk
dc.contributor.author
Torney, Colin J.
dc.date.available
2025-03-12T13:13:32Z
dc.date.issued
2023-01
dc.identifier.citation
Gaskell, Jennifer; Campioni, Nazareno; Morales, Juan Manuel; Husmeier, Dirk; Torney, Colin J.; Inferring the interaction rules of complex systems with graph neural networks and approximate Bayesian computation; The Royal Society; Journal of The Royal Society Interface; 20; 198; 1-2023; 1-11
dc.identifier.issn
1742-5662
dc.identifier.uri
http://hdl.handle.net/11336/256028
dc.description.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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
The Royal Society

dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
COLLECTIVE MOVEMENT
dc.subject
APROXIMATE BAYESIAN COMPUTATION
dc.subject
SIMULATION MODEL
dc.subject.classification
Ecología

dc.subject.classification
Ciencias Biológicas

dc.subject.classification
CIENCIAS NATURALES Y EXACTAS

dc.title
Inferring the interaction rules of complex systems with graph neural networks and approximate Bayesian computation
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2025-03-07T12:40:49Z
dc.journal.volume
20
dc.journal.number
198
dc.journal.pagination
1-11
dc.journal.pais
Reino Unido

dc.description.fil
Fil: Gaskell, Jennifer. University of Glasgow; Reino Unido
dc.description.fil
Fil: Campioni, Nazareno. University of Glasgow; Reino Unido
dc.description.fil
Fil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
dc.description.fil
Fil: Husmeier, Dirk. University of Glasgow; Reino Unido
dc.description.fil
Fil: Torney, Colin J.. University of Glasgow; Reino Unido
dc.journal.title
Journal of The Royal Society Interface
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://royalsocietypublishing.org/doi/10.1098/rsif.2022.0676
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1098/rsif.2022.0676
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