Abstracts of Software Demonstrations


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Zoomgraph
Eytan Adar
and Josh Tyler

Zoomgraph is a data-driven graph exploration project. Along with a "zoomable" user interface which lets one explore graphs on an infinite plane, Zoomgraph features an interactive language that lets users manipulate, lay out, and analyze graph structures. Graphs exist in Zoomgraph not simply as nodes and edges but entities with attributes that can be used to query, filter, and manipulate the graph. Attributes can range from employee salaries for nodes representing employees, bandwidth for communication edges, and DNA sequences forgene nodes. Zoomgraph also supports animating dynamic graphs, communication with R, and various other features. More information on the project is available at: http://www.hpl.hp.com/shl/projects/graphs/
Visualizing Augsburg Traffic Data with VanGogh
René Keller

Visualizing networks is a common task in statistics. As networks can be very large, new approaches must be tested. Techniques that just concentrate on the visualization of the network itself can give valuable information, but other graphics can help as well. This makes interactive features such as linking and selection necessary. VanGogh is a software solution that deals with networks in an interactive way to find new and interesting properties of a given dataset.

This article will give a brief introduction to VanGogh and will show how such an analysis of network data works in general. As an example dataset, I will introduce the "Augsburg Traffic" dataset, that deals with traffic flows in Augsburg.

Exploratory Data Analysis of Graphs in GGobi
Deborah F. Swayne, Duncan Temple Lang, Di Cook, and Andreas Buja
Most of the visualization software available for working with graphs has come from outside statistics and has not included the kind of interaction that statisticians have come to expect. At the same time, most of the exploratory visualization software available to statisticians has made no provision for the special structure of graphs.

Graphics software for the exploratory visual analysis of graph data should include the following: graph layout methods; a variety of displays and methods for exploring variables on both nodes and edges, including methods that allow these covariate displays to be linked to the network view; methods for thinning or otherwise trimming a large graph. In addition, the power of the visualization software is greater if it can be smoothly linked to an extensible and interactive statistics environment.

In this demo, we will show how these goals have been addressed in GGobi (www.ggobi.org) through its data format, architecture, graphical user interface design, and its relationship to the R software.

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