Tree visualization with tree-maps: 2-d space-filling approach
ACM Transactions on Graphics (TOG)
A focus+context technique based on hyperbolic geometry for visualizing large hierarchies
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The discrepancy method: randomness and complexity
The discrepancy method: randomness and complexity
Graph Visualization and Navigation in Information Visualization: A Survey
IEEE Transactions on Visualization and Computer Graphics
A Technique for Drawing Directed Graphs
IEEE Transactions on Software Engineering
Cushion Treemaps: Visualization of Hierarchical Information
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Interactive Information Visualization of a Million Items
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
SpaceTree: Supporting Exploration in Large Node Link Tree, Design Evolution and Empirical Evaluation
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
TreeJuxtaposer: scalable tree comparison using Focus+Context with guaranteed visibility
ACM SIGGRAPH 2003 Papers
DOITrees revisited: scalable, space-constrained visualization of hierarchical data
Proceedings of the working conference on Advanced visual interfaces
Expand-Ahead: A Space-Filling Strategy for Browsing Trees
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Elastic Hierarchies: Combining Treemaps and Node-Link Diagrams
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Degree-of-interest trees: a component of an attention-reactive user interface
Proceedings of the Working Conference on Advanced Visual Interfaces
Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines
IEEE Transactions on Visualization and Computer Graphics
Scalable, robust visualization of very large trees
EUROVIS'05 Proceedings of the Seventh Joint Eurographics / IEEE VGTC conference on Visualization
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Given a very large, node-weighted, rooted tree on, say, n nodes, if one has only enough space to display a k-node summary of the tree, what is the most informative way to draw the tree? We define a type of weighted tree that we call a summary tree of the original tree that results from aggregating nodes of the original tree subject to certain constraints. We suggest that the best choice of which summary tree to use (among those with a fixed number of nodes) is the one that maximizes the information-theoretic entropy of a natural probability distribution associated with the summary tree, and we provide a (pseudopolynomial-time) dynamic-programming algorithm to compute this maximum entropy summary tree, when the weights are integral. The result is an automated way to summarize large trees and retain as much information about them as possible, while using (and displaying) only a fraction of the original node set. We illustrate the computation and use of maximum entropy summary trees on five real data sets whose weighted tree representations vary widely in structure. We also provide an additive approximation algorithm and a greedy heuristic that are faster than the optimal algorithm, and generalize to trees with real-valued weights.