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Automatic Hypothesis Generation for Network Growth Models

Computer Science: Two possible internships Context Networks have become a fundamental abstraction for modeling systems across many scientific fields. Plausible hypothesis describing their growth processes can help us understand a wide range of phenomena, but formulating such hypothesis is often challenging and requires insights that may be counter-intuitive. In the last years, we have developed an approach to automatically discover realistic 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 a mathematical function of arbitrary complexity [1]. As the proposed method is completely general […]