Symbolic regression of generative network models
| Autor: | Camille Roth, Telmo Menezes |
|---|---|
| Přispěvatelé: | Roth, Camille, SYSCOMM : Systèmes complexes et modélisation mathématique - Simulations de parenté : modélisation de la dynamique matrimoniale et mnémonique dans les réseaux de parenté - - SimPa2009 - ANR-09-SYSC-0013 - SYSCOMM - VALID, Contenus et Interactions - Politique des algorithmes - - Algopol2012 - ANR-12-CORD-0018 - CONTINT - VALID, Centre d'Analyse et de Mathématique sociales (CAMS), École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), Centre Marc Bloch (CMB), Ministère de l'Europe et des Affaires étrangères (MEAE)-Bundesministerium für Bildung und Forschung-Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.)-Centre National de la Recherche Scientifique (CNRS), ANR-12-CORD-0018,Algopol,Politique des algorithmes(2012), ANR-09-SYSC-0013,SimPa,Simulations de parenté : modélisation de la dynamique matrimoniale et mnémonique dans les réseaux de parenté(2009) |
| Rok vydání: | 2014 |
| Předmět: |
FOS: Computer and information sciences
Empirical data Theoretical computer science [SHS.SOCIO] Humanities and Social Sciences/Sociology Computer science 02 engineering and technology 01 natural sciences Machine Learning [NLIN.NLIN-AO] Nonlinear Sciences [physics]/Adaptation and Self-Organizing Systems [nlin.AO] Evolutionary Computing Software [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] [SHS.STAT] Humanities and Social Sciences/Methods and statistics 0202 electrical engineering electronic engineering information engineering Scientific Data Network model [SHS.SOCIO]Humanities and Social Sciences/Sociology [SHS.STAT]Humanities and Social Sciences/Methods and statistics Multidisciplinary Applied Mathematics Computer Science - Neural and Evolutionary Computing Computer Science - Social and Information Networks 020201 artificial intelligence & image processing Physics - Physics and Society [SCCO.COMP]Cognitive science/Computer science FOS: Physical sciences Physics and Society (physics.soc-ph) Article Evolutionary computation Artificial Intelligence [SCCO.COMP] Cognitive science/Computer science 0103 physical sciences Animals Computer Simulation Neural and Evolutionary Computing (cs.NE) Caenorhabditis elegans [NLIN.NLIN-AO]Nonlinear Sciences [physics]/Adaptation and Self-Organizing Systems [nlin.AO] 010306 general physics Social and Information Networks (cs.SI) Social network business.industry Genetic Algorithms Models Theoretical [STAT.ML] Statistics [stat]/Machine Learning [stat.ML] Nerve Net Networks Symbolic regression business Generative grammar |
| Zdroj: | Scientific Reports Scientific Reports, Nature Publishing Group, 2014, 4, pp.6284 Scientific Reports, Nature Publishing Group, 2014, 4 (6284), pp.7 |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/srep06284 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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