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

每个网络数据集都提出了独特的挑战，但是有越来越多的方法可以适应特定的情况。本研究将通过继续发展机器学习思想来扩展该工具包，该思想执行积极的局部变量选择以适应局部指标，从而允许附近节点具有类似的边缘形成模型，但远程节点具有非常不同的模型。此外，该研究还将为基于最小描述长度推理的网络模型的拟合优度评估提供一种新的方法。网络建模已经成为许多科学领域的重要方法论，包括生物化学、社会学和互联网传播。重要的应用包括导致狒狒群体裂变的社会互动，从蛋白质相互作用中获得的生化知识，以及对维基百科的成长和结构的洞察。这项研究将为网络增长开发新的统计模型，以及评估它们如何很好地解释给定数据集的新方法。

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.