Understanding Multi-touch Manipulation for Surface Computing
INTERACT '09 Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II
Graph drawing aesthetics in user-sketched graph layouts
AUIC '10 Proceedings of the Eleventh Australasian Conference on User Interface - Volume 106
Exploring the relative importance of crossing number and crossing angle
Proceedings of the 3rd International Symposium on Visual Information Communication
Supporting Search Result Browsing and Exploration via Cluster-Based Views and Zoom-Based Navigation
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Do Mechanical Turks dream of square pie charts?
Proceedings of the 3rd BELIV'10 Workshop: BEyond time and errors: novel evaLuation methods for Information Visualization
The readability of path-preserving clusterings of graphs
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Patterns for visualization evaluation
Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization
Improving multiple aesthetics produces better graph drawings
Journal of Visual Languages and Computing
Hi-index | 0.00 |
Many graph layout algorithms optimize visual characteristics to achieve useful representations. Implicitly, their goal is to create visual representations that are more intuitive to human observers. In this paper, we asked users to explicitly manipulate nodes in a network diagram to create layouts that they felt best captured the relationships in the data. This allowed us to measure organizational behavior directly, allowing us to evaluate the perceptual importance of particular visual features, such as edge crossings and edge-lengths uniformity. We also manipulated the interior structure of the node relationships by designing data sets that contained clusters, that is, sets of nodes that are strongly interconnected. By varying the degree to which these clusters were “masked” by extraneous edges we were able to measure observers’ sensitivity to the existence of clusters and how they revealed them in the network diagram. Based on these measurements we found that observers are able to recover cluster structure, that the distance between clusters is inversely related to the strength of the clustering, and that users exhibit the tendency to use edges to visually delineate perceptual groups. These results demonstrate the role of perceptual organization in representing graph data and provide concrete recommendations for graph layout algorithms.