NIH-NSF Visualization Research Challenges report summary.
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Biotechnology↗
Biomedical subjects
Tamara Munzner
Publications and source records attributed to Tamara Munzner.
Composite rectilinear deformation for stretch and squish navigation.
We present the first scalable algorithm that supports the composition of successive rectilinear deformations. Earlier systems that provided stretch and squish navigation could only handle small datasets. More recent work featuring rubber sheet navigation for large datasets has focused on rendering and on application-specific issues. However, no algorithm has yet been presented for carrying out such navigation methods; our paper addresses this problem. For maximum flexibility with large datasets, a stretch and squish navigation algorithm should allow for millions of potentially deformable regions. However, typical usage only changes the extents of a small subset k of these n regions at a time. The challenge is to avoid computations that are linear in n, because a single deformation can affect the absolute screen-space location of every deformable region. We provide an O(klogn) algorithm that supports any application that can lay out a dataset on a generic grid, and show an implementation that allows navigation of trees and gene sequences with millions of items in sub-millisecond time.
Journal Article↗
Smashing peacocks further: drawing quasi-trees from biconnected components.
Quasi-trees, namely graphs with tree-like structure, appear in many application domains, including bioinformatics and computer networks. Our new SPF approach exploits the structure of these graphs with a two-level approach to drawing, where the graph is decomposed into a tree of biconnected components. The low-level biconnected components are drawn with a force-directed approach that uses a spanning tree skeleton as a starting point for the layout. The higher-level structure of the graph is a true tree with meta-nodes of variable size that contain each biconnected component. That tree is drawn with a new area-aware variant of a tree drawing algorithm that handles high-degree nodes gracefully, at the cost of allowing edge-node overlaps. SPF performs an order of magnitude faster than the best previous approaches, while producing drawings of commensurate or improved quality.
Journal Article↗
TopoLayout: multilevel graph layout by topological features.
We describe TopoLayout, a feature-based, multilevel algorithm that draws undirected graphs based on the topological features they contain. Topological features are detected recursively inside the graph, and their subgraphs are collapsed into single nodes, forming a graph hierarchy. Each feature is drawn with an algorithm tuned for its topology. As would be expected from a feature-based approach, the runtime and visual quality of TopoLayout depends on the number and types of topological features present in the graph. We show experimental results comparing speed and visual quality for TopoLayout against four other multilevel algorithms on a variety of data sets with a range of connectivities and sizes. TopoLayout frequently improves the results in terms of speed and visual quality on these data sets.
Algorithms↗