In what is a cool mainstream find, Russian cosmonauts have discovered LIFE clinging to the outside of the International Space Station. This is the first time living organisms have been found on the space station and scientists are not sure how it got there. The Creatures The creatures were found during a space walk to […]
Physicists have begun to explore the idea that mass and length may not be fundamental properties of nature. The hypothesis could help to avoid the conclusion that our world is just a weird bubble in an endlessly foaming multiverse.
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.