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Article dans une revue Scientific Reports Année : 2014

Symbolic regression of generative network models

Résumé

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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halshs-01101038, version 1 (07-01-2015)

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  • HAL Id : halshs-01101038 , version 1

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Telmo Menezes, Camille Roth. Symbolic regression of generative network models. Scientific Reports, 2014, 4 (6284), pp.7. ⟨halshs-01101038⟩
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