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

合作提案：分析大型生物和信息网络复杂性和增长的统计方法

• 批准号: 1106980
• 负责人: Edoardo Airoldi
• 金额: $ 7.5万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1106980&HistoricalAwards=false
• 关键词: Collaborative; proposal; Statistical; methods; analyzing

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.

每个网络数据集都面临着独特的挑战，但有越来越多的方法可以适应特定情况。这项研究将通过继续开发机器学习思想来扩展该工具包，这些思想执行积极的局部变量选择以适应局部度量，从而允许附近的节点具有类似的边缘形成模型，但远处的节点具有非常不同的模型。同时，该研究也为基于最小描述长度推理的网络模型拟合优度评估提供了一种新的方法。网络建模已经成为许多科学领域的关键方法，包括生物化学，社会学和互联网通信。重要的应用包括社会互动导致狒狒部队的裂变，来自蛋白质-蛋白质相互作用的生物化学知识，以及对维基百科的增长和结构的洞察。这项研究将开发新的网络增长统计模型，以及评估它们如何解释给定数据集的新方法。