title: Symbolic regression of generative network models.
abstract: 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.
authors.affiliation: 1] Centre Marc Bloch Berlin (An-Institut der Humboldt Universität, UMIFRE CNRS-MAE) Friedrichstr. 191, 10117 Berlin, Germany [2] Centre d'Analyse et de Mathématique Sociales (UMR 8557 CNRS-EHESS) 190 av. de France, 75013 Paris, France.
authors.forename: Telmo
authors.initials: T
authors.lastname: Menezes
authors.affiliation: Centre Marc Bloch Berlin (An-Institut der Humboldt Universität, UMIFRE CNRS-MAE) Friedrichstr. 191, 10117 Berlin, Germany.
authors.forename: Camille
authors.initials: C
authors.lastname: Roth
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D017173:Caenorhabditis elegans / Q000033:anatomy & histology; D003198:Computer Simulation; D008962:Models, Theoretical; D009415:Nerve Net