FODAVA: Collaborative Research: Foundations of Comparative Analytics for Uncertainty in Graphs

FODAVA：协作研究：图形不确定性比较分析的基础

• 批准号: 0937073
• 负责人: Alex Pang
• 金额: $ 13.75万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0937073&HistoricalAwards=false
• 关键词: FODAVA; Collaborative; Research; Foundations; Comparative

This is a collaborative research effort bringing together expertise of Lise Getoor, University of Maryland College Park (0937094), Alex Pang, University of California-Santa Cruz (0937073) and Lisa Singh, Georgetown University (0937070).In today's linked world, graphs and networks abound. There are communication networks, social networks, financial transaction networks, gene regulatory networks, disease transmission networks, ecological food networks, sensor networks and more. Observational data describing these networks can often times be obtained; unfortunately, this graph data is usually noisy and uncertain. In this research, we propose a formalism which allows us to capture and reason about the inherent uncertainty and imprecision in an underlying graph. We begin by proposing probabilistic similarity logic (PSL), a simple, yet powerful, language for describing problems which require probabilistic reasoning about similarity in networked data. We also introduce the notion of visual comparative analysis of PSL models derived using different evidence and assumptions, and illustrate its utility for the analysis of graphs and networks. Dealing with noise and uncertainty in complex domains, and conducting comparative analytics are core capabilities required for the Foundations on Data Analysis and Visual Analytics (FODAVA) mission. This research focuses on integrating representation, comparative analysis and visualizations methods into an open source toolkit that supports the representation, comparison and visualization of PSL models. In addition to producing the toolkit, the research team is working with researchers in a variety of interdisciplinary domains to validate the utility of our approach, and also developing tutorial and training materials for the tools. The key broader impact of the work is that the methods for reasoning about sources of noise and uncertainty in graphs, and understanding their impact on results are general and fundamental to the intelligent analysis of today's rich information sources. Results, including open source software will be distributed via the project Web site ( http://www.cs.umd.edu/projects/linqs/fodava/ ).

这是一项合作研究，汇集了马里兰大学帕克分校的Lise Getoor（0937094）、加州大学圣克鲁斯分校的Alex Pang（0937073）和乔治城大学的Lisa Singh（0937070）的专业知识。在今天这个相互联系的世界里，图表和网络比比皆是。有通信网络、社会网络、金融交易网络、基因调控网络、疾病传播网络、生态食品网络、传感器网络等等。通常可以获得描述这些网络的观测数据；不幸的是，这种图形数据通常是嘈杂和不确定的。在这项研究中，我们提出了一种形式主义，它允许我们捕捉和推理底层图中固有的不确定性和不精确性。我们首先提出概率相似逻辑（PSL），这是一种简单但功能强大的语言，用于描述需要对网络数据相似性进行概率推理的问题。我们还介绍了使用不同证据和假设得出的PSL模型的视觉比较分析的概念，并说明了它在图和网络分析中的实用性。处理复杂领域中的噪声和不确定性，以及进行比较分析是数据分析和视觉分析基础（FODAVA）任务所需的核心能力。本研究的重点是将表示、比较分析和可视化方法集成到一个开源工具包中，该工具包支持PSL模型的表示、比较和可视化。除了制作工具包之外，研究团队还与各种跨学科领域的研究人员合作，以验证我们的方法的实用性，并为这些工具开发教程和培训材料。这项工作的关键影响是，对图中的噪声和不确定性来源进行推理的方法，以及理解它们对结果的影响，是对当今丰富信息源的智能分析的一般和基础。结果，包括开源软件将通过项目网站（http://www.cs.umd.edu/projects/linqs/fodava/）发布。