A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations

By M. Ghoniem, Jean-Daniel Fekete, Philippe Castagliola. IEEE Information Visualization Conference, 2004

454 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4effb869de6edd5e6e7e1ec476ab402fa9d28c55

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Ben Shneiderman
2. Jeffrey Heer
3. Lydia B. Chilton
4. Aleks Aris
5. D. boyd
6. Jean-Daniel Fekete
7. Tamara Munzner
8. Reijo Savolainen

In Information Systems

1. Ben Shneiderman
2. Jeffrey Heer
3. Lydia B. Chilton
4. Aleks Aris
5. D. boyd

In Signal Processing

1. Jeffrey Heer
2. Ben Shneiderman
3. Aleks Aris
4. J. C. Roberts

Citations in the corpus

• Tabulator: Exploring and Analyzing linked data on the Semantic Web (2006, background): “Another related approach is Ghoniem’s work [21], which deals with visualizing large complex graphs with single arc types but their matrix form is different from the table view used in Tabulator.”
• Vizster: visualizing online social networks (2005, background): “Though adjacency matrix-based views have shown promise for visualizing larger networks (at least among very specialized, technical users [9]), their unfamiliarity to our prototypical users and their single degree of freedom for spatially organizing members (thus limiting community groupings) make th”
• Network Visualization by Semantic Substrates (2006, methodology): “A different strategy is to use matrix-based representations instead of node-link diagrams ([17], [1]).”