CHI '86 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Structural analysis of hypertexts: identifying hierarchies and useful metrics
ACM Transactions on Information Systems (TOIS)
Navigating hierarchically clustered networks through fisheye and full-zoom methods
ACM Transactions on Computer-Human Interaction (TOCHI)
Systems of functional equations
Random Structures & Algorithms - Special issue: average-case analysis of algorithms
Information visualization: perception for design
Information visualization: perception for design
Graph Drawing: Algorithms for the Visualization of Graphs
Graph Drawing: Algorithms for the Visualization of Graphs
Graph Visualization and Navigation in Information Visualization: A Survey
IEEE Transactions on Visualization and Computer Graphics
Reduction of Visual Complexity in Dynamic Graphs
GD '94 Proceedings of the DIMACS International Workshop on Graph Drawing
Multilevel Visualization of Clustered Graphs
GD '96 Proceedings of the Symposium on Graph Drawing
Dynamic Hierarchy Specification and Visualization
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Navigating Hierarchies with Structure-Based Brushes
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Random generation of dags for graph drawing
Random generation of dags for graph drawing
Automatic generation of interactive overview diagrams for the navigation of large graphs
Automatic generation of interactive overview diagrams for the navigation of large graphs
Tree Visualisation and Navigation Clues for Information Visualisation
Tree Visualisation and Navigation Clues for Information Visualisation
FAST: a multi-processed environment for visualization of computational fluid dynamics
VIS '90 Proceedings of the 1st conference on Visualization '90
A tool for visualizing the topology of three-dimensional vector fields
VIS '91 Proceedings of the 2nd conference on Visualization '91
Case Study: E-Commerce Clickstream Visualization
INFOVIS '01 Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01)
Discovering parametric clusters in social small-world graphs
Proceedings of the 2005 ACM symposium on Applied computing
Visualization of clustered directed acyclic graphs with node interleaving
Proceedings of the 2009 ACM symposium on Applied Computing
Node overlap removal in clustered directed acyclic graphs
Journal of Visual Languages and Computing
Multiscale scatterplot matrix for visual and interactive exploration of metabonomic data
VIEW'06 Proceedings of the 1st first visual information expert conference on Pixelization paradigm
Graph averaging as a means to compare multichannel EEG coherence networks
EG VCBM'10 Proceedings of the 2nd Eurographics conference on Visual Computing for Biology and Medicine
See what you know: analyzing data distribution to improve density map visualization
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
Functional unit maps for data-driven visualization of high-density EEG coherence
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
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Two tasks in Graph Visualization require partitioning: the assignment of visual attributes and divisive clustering. Often, we would like to assign a color or other visual attributes to a node or edge that indicates an associated value. In an application involving divisive clustering, we would like to partition the graph into subsets of graph elements based on metric values in such a way that all subsets are evenly populated. Assuming a uniform distribution of metric values during either partitioning or coloring can have undesired effects such as empty clusters or only one level of emphasis for the entire graph. Probability density functions derived from statistics about a metric can help systems succeed at these tasks.