FRG: Collaborative Research: Unified statistical theory for the analysis and discovery of complex networks

FRG：协作研究：用于分析和发现复杂网络的统一统计理论

• 批准号: 1160319
• 负责人: Peter Bickel
• 金额: $ 119.99万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1160319&HistoricalAwards=false
• 关键词: FRG; Collaborative; Research; Unified; statistical

The investigators will develop a unified nonparametric theoretical framework to study stochastic network models and design scalable algorithms (and software) to fit these models. They intend to carry out the development and validation of their methods with collaborators in biology who have gathered extensive new data for the assessment of protein structure and determination of biological pathways particularly in Drosophila. Such problems are omnipresent in genomics and they expect their methods to carry over widely. They will also use networks with uncertainty measures to study relationships between words and phrases in a newspaper database in order to provide media analysts with automatic and scalable algorithms. In all cases they will focus on methods of general statistical confidence which have been lacking in work so far.Our world is connected through relationships, among "actors" who can be people, organizations, words, genes, proteins, and more. Advancements of information technology have enabled collection of massive amounts of data in all disciplines for us to build relationships between these actors. These relationships can be effectively described as networks, and properties or patterns in these networks can be random or knowledge. Responding to this recent data availability and a huge potential for knowledge discovery, research in networks is attracting much attention from researchers in physics, social science, computer science, and probability. While contributing to the development of core statistical research, the proposed research will directly impact the interdisciplinary field of network analysis and the study of complex networks. The applications of their research results are diverse and well beyond the two fields studied in the proposal: genomics and media analysis. They include national security, communications, sociology, political science, and infectious disease. The statistical tools developed are unifying and could change how many scientists approach network analysis. As a result, statistical research will become more prominent in the networks community.

研究人员将开发一个统一的非参数理论框架来研究随机网络模型，并设计可扩展的算法（和软件）来适应这些模型。他们打算与生物学领域的合作者一起开发和验证他们的方法，这些合作者已经收集了广泛的新数据，用于评估蛋白质结构和确定生物学途径，特别是在果蝇中。这样的问题在基因组学中无处不在，他们希望他们的方法能广泛应用。 他们还将使用具有不确定性度量的网络来研究报纸数据库中单词和短语之间的关系，以便为媒体分析师提供自动和可扩展的算法。在所有的情况下，他们将集中在一般的统计信心的方法，一直缺乏在工作迄今为止。我们的世界是通过关系，“演员”谁可以是人，组织，文字，基因，蛋白质，等等。信息技术的进步使我们能够在所有学科中收集大量数据，以便在这些行为者之间建立关系。这些关系可以有效地描述为网络，这些网络中的属性或模式可以是随机的或知识的。由于最近的数据可用性和知识发现的巨大潜力，网络研究吸引了物理学，社会科学，计算机科学和概率学研究人员的广泛关注。在促进核心统计研究的发展的同时，拟议的研究将直接影响网络分析和复杂网络研究的跨学科领域。他们的研究成果的应用是多样化的，远远超出了两个领域的研究建议：基因组学和媒体分析。它们包括国家安全，通信，社会学，政治学和传染病。开发的统计工具是统一的，可能会改变多少科学家接近网络分析。因此，统计研究将在网络社区中变得更加突出。