CAREER: Graph Identification

职业：图形识别

• 批准号: 0746930
• 负责人: Lise Getoor
• 金额: $ 50万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746930&HistoricalAwards=false
• 关键词: CAREER; Graph; Identification

Today's world requires that we participate in, comprehend, or otherwise navigate many kinds of networks, including social networks, communication networks, financial transaction networks, gene regulatory networks, disease transmission networks, ecological food networks, sensor networks and more. Observational data on such networks is plentiful, but extracting the relevant data is challenging. Furthermore, this data often has errors, is incomplete and fragmented, or otherwise does not offer a concise and big-picture view of the network that we want to understand and use. This research project will develop methods for inferring and representing the essential, underlying network structure that best accounts for observed entities and interactions between these entities. In particular, the proposal introduces a process abstraction called ?graph identification,? which is composed of more basic processes for identifying observed entities that are equivalent, identifying links (i.e., relationships) between entities from observed interactions, and simultaneously classifying many entities into classes based on their interconnection structure. A computational challenge of this project is integrating these basic processes, each of which has been studied by the PI and others, to achieve accurate solutions to the overall graph identification problem, in a manner that is efficient and scalable. This research has broader impacts for basic science, national security and a better understanding of privacy issues in today's networked society.

当今的世界要求我们参与，理解或以其他方式浏览许多类型的网络，包括社交网络，通信网络，金融交易网络，基因调节网络，疾病传输网络，生态食品网络，传感器网络等。关于此类网络的观察数据很丰富，但是提取相关数据具有挑战性。此外，此数据通常具有错误，不完整且分散，或者以其他方式没有提供我们想要理解和使用的网络的简洁明了的视图。该研究项目将开发用于推断和代表基本基础网络结构的方法，该结构最能说明这些实体之间观察到的实体和相互作用。特别是，该提案引入了一个称为“图形识别”的过程抽象，这是由更基本的过程组成的，用于识别与等效的观察到的实体，从观察到的相互作用中识别实体之间的链接（即关系），并根据其互连结构同时将许多实体分类为类。该项目的一项计算挑战是整合了这些基本过程，每个过程已经由PI和其他过程进行了研究，以实现对整体图形识别问题的准确解决方案，以有效且可扩展的方式。这项研究对基础科学，国家安全和对当今网络社会的隐私问题有更好的了解产生了更大的影响。