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Symbolic regression of generative network models

Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requir...

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Detalles Bibliográficos
Autores principales: Menezes, Telmo, Roth, Camille
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4155339/
https://www.ncbi.nlm.nih.gov/pubmed/25190000
http://dx.doi.org/10.1038/srep06284
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author Menezes, Telmo
Roth, Camille
author_facet Menezes, Telmo
Roth, Camille
author_sort Menezes, Telmo
collection PubMed
description Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied “out of the box” to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world networks. We were able to find programs that are simple enough to lead to an actual understanding of the mechanisms proposed, namely for a simple brain and a social network.
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spelling pubmed-41553392014-09-10 Symbolic regression of generative network models Menezes, Telmo Roth, Camille Sci Rep Article Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied “out of the box” to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world networks. We were able to find programs that are simple enough to lead to an actual understanding of the mechanisms proposed, namely for a simple brain and a social network. Nature Publishing Group 2014-09-05 /pmc/articles/PMC4155339/ /pubmed/25190000 http://dx.doi.org/10.1038/srep06284 Text en Copyright © 2014, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Menezes, Telmo
Roth, Camille
Symbolic regression of generative network models
title Symbolic regression of generative network models
title_full Symbolic regression of generative network models
title_fullStr Symbolic regression of generative network models
title_full_unstemmed Symbolic regression of generative network models
title_short Symbolic regression of generative network models
title_sort symbolic regression of generative network models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4155339/
https://www.ncbi.nlm.nih.gov/pubmed/25190000
http://dx.doi.org/10.1038/srep06284
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