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Ph.D. Theses

Matrix Visualization of Graphs

By Matthew Schumaker
Advisor: Michael M. Danchak
April 21, 2004

In recent years both industry and academia have started to produce massive amounts of data. Computers have played a key role in both the production and storage of this data. Much of this data comes in the form of relational information that codifies connections between objects. The data may hold relationships about genes, consumer shopping habits, economic indicators or terrorists. The traditional means of storing these relationships is in a graph. The knowledge that can be obtained from these graphs carries both academic and economic value.

However, displaying these graphs can be troublesome as more and more information is added. Many techniques in use today do not do an adequate job of maximizing what is presented to the user. For this reason we investigated a novel visualization technique called ``Matrix Visualization''. This technique presents graph information in a very dense manner. To perform this investigation we developed a software architecture to create the visualization then performed a validation study to compare it to traditional graph visualization techniques for three common tasks: cluster identification, tree level identification and intra-level relationship identification.

After doing the study, we found that the matrix visualization out performs traditional visualization techniques in terms of speed or accuracy for these tasks, in some cases by as much as a factor of two. This work contributes a foundation of new understanding of this little researched technique as well as adds to the existing literature new knowledge of traditional graph visualization.

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