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: 0937094
• 负责人: Lise Getoor
• 金额: $ 26.25万
• 依托单位: University of Maryland, College Park
• 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=0937094&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/ ).

这是一项合作研究工作，汇集了马里兰州大学帕克分校的Lise Getoor（0937094）、加州大学圣克鲁斯分校的Alex Pang（0937073）和乔治敦大学的丽莎辛格（0937070）的专业知识。有通信网络、社交网络、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。描述这些网络的观测数据通常可以获得;不幸的是，这些图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，它使我们能够捕捉和推理内在的不确定性和不精确性的基础图形。我们开始提出概率相似性逻辑（PSL），一个简单的，但功能强大，语言描述的问题，需要概率推理的相似性在网络数据。我们还介绍了使用不同的证据和假设得出的PSL模型的视觉比较分析的概念，并说明其效用的图形和网络的分析。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可视化分析基础（FODAVA）使命所需的核心能力。本研究的重点是集成表示，比较分析和可视化方法到一个开源的工具包，支持PSL模型的表示，比较和可视化。除了制作工具包外，研究团队还与各种跨学科领域的研究人员合作，以验证我们方法的实用性，并为工具开发教程和培训材料。这项工作的主要影响是，对图形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，对于当今丰富信息源的智能分析是通用和基本的。包括开放源码软件在内的成果将通过项目网站（http://www.cs.umd.edu/projects/linqs/fodava/）分发。