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Foundations of Comparative Analytics for Uncertainty in Graphs
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/projects/fodova/).