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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  
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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