Collaborative proposal: Statistical methods for analyzing complexity and growth of large biological and information networks

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Every network dataset poses unique challenges, but there is a growing toolkit of methods that can be adapted to specific situations. This research will extend that toolkit by continuing the development of machine learning ideas that perform aggressive local variable selection to fit local metrics, thus allowing nearby nodes to have similar models for edge formation, but distant nodes to have very different models. Also, the research will provide a new approach to goodness-of-fit assessment for network models, based upon minimum description length inference. Network modeling has emerged as a critical methodology across many fields of science, including biochemistry, sociology, and Internet communication. Important applications include social interactions leading to fission in baboon troops, biochemical knowledge derived from protein-protein interactions, and insight into the growth and structure of the Wikipedia. This research will develop novel statistical models for network growth and new ways to assess how well they explain a given dataset.