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Carlea Holl-Jensen||cholljen@umd.edu


HCIL-2012-16

Shneiderman, B., Dunne, C. (August 2012)
Interactive Network Exploration to Derive Insights: Filtering, Clustering, Grouping, and Simplification
Published in: GD '12: Proc. 20th International Symposium on Graph Drawing
HCIL-2012-16

The growing importance of network analysis has increased attention on interactive exploration to derive insights and support personal, business, legal, scientific, or national security decisions. Since networks are often complex and cluttered, strategies for effective filtering, clustering, grouping, and simplification are helpful in finding key nodes and links, surprising clusters, important groups, or meaningful patterns. We describe readability metrics and strategies that have been implemented in NodeXL, our free and open source network analysis tool, and show examples from our research. While filtering, clustering, and grouping have been used in many tools, we present several advances on these techniques. We also discuss our recent work on motif simplification, in which common patterns are replaced with compact and meaningful glyphs, thereby improving readability.



Keshif:  Simplicity Driven Visual Faceted Browser Screenshot

Keshif: Simplicity Driven Visual Faceted Browser
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