2014/09/05 by Telmo Menezes, Camille Roth
Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Complex Network Analysis Techniques #Computer science #Evolutionary Algorithms and Applications #Generative grammar #Machine learning #Mathematics #Opinion Dynamics and Social Influence #Regression #Regression analysis #Statistics #Symbolic regression #cs.NE #cs.SI #physics.soc-ph
paper · pdf · doi:10.1038/srep06284
published as Scientific Reports volume 4, Article number: 6284 (2015)
openalex publication_date 2014/09/05 · arxiv created 2014/09/08 · arxiv updated 2020/04/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.