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.

当今世界要求我们参与、理解或以其他方式驾驭多种网络，包括社交网络、通信网络、金融交易网络、基因调控网络、疾病传播网络、生态食物网络、传感器网络等等。此类网络的观测数据非常丰富，但提取相关数据具有挑战性。此外，这些数据通常存在错误，不完整和碎片化，或者无法提供我们想要了解和使用的网络的简洁和全局视图。该研究项目将开发用于推断和表示基本的底层网络结构的方法，这些网络结构最好地解释了观察到的实体和这些实体之间的相互作用。特别是，该提案引入了一个过程抽象称为？图形识别，？其由用于识别等效的观察实体，识别链接（即，关系），并同时基于它们的互连结构将许多实体分类到类中。该项目的一个计算挑战是整合这些基本过程，PI和其他人已经研究了其中的每一个过程，以高效和可扩展的方式实现整个图识别问题的准确解决方案。这项研究对基础科学、国家安全和更好地理解当今网络社会的隐私问题具有更广泛的影响。