Graphs and networks

A cladogram is an N-ary tree showing multiple possible descendant paths from a common biological ancestor. Cladograms are inferred based upon available evidence and informed speculation about which species likely evolved from which other species. However, the same ancestor and descendants may be linked together in multiple N-ary trees, each an alternate proposed solution from available evidence.

This 3D visualization presents a "tree" of alternate cladograms arranged to enable comparisons of competing solutions. The cladograms are each built by parsing a text-based phylogenetic equation that lists species and descendant groups. Any number of competing trees can be added to the diagram. Colors indicate similarities between the trees. The trees can be dragged around in the circle of trees and the scene re-oriented in 3D.

This project was funded by the National Science Foundation. Development was in C++ and VRML.

A network is a multilayered structure of hubs and their descendants. Unlike traditional 2D network maps overlaid atop a US or world map, this diagram focuses upon connectivity, not geo-layout. This enables a more efficient representation that doesn't try to cram a lot of information into a small space (such as all hubs in the L.A. area while looking at at the whole US map).

This visualization was built dynamically from network topology data assembled by a network monitoring organization. The layout used geographic information to place hubs that were located in the same part of the country near each other in each circle of nodes at each layer of the diagram. Major hubs are at higher layers. This visualization also supported network traffic statistics overlaid atop the graph and animated to observe traffic ebb and flow and to highlight bottlenecks.

This project was funded by the National Science Foundation. Development was in C++ and VRML.

When chemical 'A' interacts with 'B' to form 'C', then 'C' interacts with 'D' to form 'E', this chain of interactions forms a pathway that can be shown as a graph. When investigating the inner workings of the cell, researchers draw complex hypothetical pathways. If we view this chain-reaction as sending a chemical signal from the starting chemical to the ending one, then the diagram shows possible signal pathways and the concentration plots the strength of that signal. Such a pathway is used in cell signaling research. This visualization shows a complex pathway centered on Arachadonic Acid.

This visualization plotted biological pathways dynamically based upon data in an Oracle database. Pathways also could be drawn from scratch by dragging and dropping chemical nodes and drawing connections between them to designate interactions between them. Color indicates the type of chemical involved and the type of interaction. The pathway could be queried to obtain information on chemical compounds in the graph. Experimental data showing changes in chemical concentrations were overlaid atop the pathway and animated to show the order in which interactions occur.

This project was funded by the National Science Foundation. Development was in Java and JOGL.