IEEE TCDL Bulletin
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TCDL Bulletin
Volume 3   Issue 2
Summer 2007


Exploring Content-Actor Paired Network Data
Using Iterative Query Refinement with NetLens

Hyunmo Kang, Catherine Plaisant, Benjamin B. Bederson
Human-Computer Interaction Lab
Department of Computer Science
University of Maryland Institute for Advanced Computer Studies
College Park, MD 20742
{kang, plaisant, bederson}


Networks have remained a challenge for information retrieval and visualization because of the rich set of tasks that users want to accomplish. Most visualization research on understanding relationships in large datasets implicitly assumes that a node link diagram is appropriate. However, we believe that while node-link diagrams have their place, they don't scale up well and too often produce cluttered overviews with few readable labels, and often have difficulties supporting even the simplest tasks such as reviewing the papers that cite a selected paper.

In this research, we took a completely different approach in designing NetLens by using multiple simple coordinated views of ordered lists and histogram overviews to represent a Content-Actor model of information. Examples of Content-Actor pairs of interest to the visual analytics community include scientific publications and authors, emails and people, legal cases and courts, intelligence reports and countries, etc. In all those examples, both the content and actors consist of networked data such as reports citing other reports, authors having advisors or co-authors. NetLens shows paired networks of content and actors in coordinated views and allows users to refine their queries by transferring filtered data in one entity window to the other iteratively.

Thumbnail image of poster

For a larger view of Figure 1, click here.

More information about NetLens project: <>

NetLens video demo: <>.


© Copyright 2007 Hyunmo Kang, Catherine Plaisant, and Benjamin B. Bederson
Some or all of these materials were previously published in the Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital libraries, ACM 1-59593-354-9.

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