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From the excellent survey paper, http://www.cwi.nl/InfoVisu/Survey/StarGraphVisuInInfoVis.pdf, which is part of http://www.cwi.nl/InfoVisu...
- Information visualization has become a large field and “sub–fields” are beginning to emerge. A simple way to determine the applicability of graph visualization is to consider the following question: is there an inherent relation among the data elements to be visualized? If the answer to the question is “no”, than data elements are “unstructured” and the goal of the information visualization system might be to help discover relations among data through visual means. If, however, the answer to the question is “yes”, then the data can be represented by the nodes of a graph, with the edges representing the relations.
- Graph visualization has many areas of application. Most people have encountered a file hierarchy on a computer system. A file hierarchy can be represented as a tree (a special type of graph). It is often necessary to navigate through the file hierarchy in order to find a particular file. Anyone who has done this has probably experienced a few of the problems involved in graph visualization: “Where am I?” “Where is the file that I'm looking for?” Other familiar types of graphs include the hierarchy illustrated in an organisational chart and taxonomies that portray the relations between species. Web site maps are another application of graphs as well as browsing history. In biology and chemistry, graphs are applied to evolutionary trees, phylogenetic trees, molecular maps, genetic maps, biochemical pathways, and protein functions. Other areas of application include object–oriented systems (class browsers), data structures (compiler data structures in particular), real–time systems (state–transition diagrams, Petri nets), data flow diagrams, subroutine–call graphs, entity relationship diagrams (e.g. UML and database structures), semantic networks and knowledge–representation diagrams, project management (PERT diagrams), logic programming (SLD–trees), VLSI (circuit schematics), virtual reality (scene graphs), and document management systems. Note that the information isn’t always guaranteed to be in a purely hierarchical format — this necessitates techniques which can deal with more general graphs than trees.
See more in the [CategoryInformationVisualization].
Glossary of Terms
- GeometricZoom?. The normal concept of zooming in graphical application. This involves changing the world transform such that coordinates are scaled outwards. For instance, in an affine transform, this would be some diagonal matrix.
- SemanticZoom?. Adding more detail as you zoom in, in the vein of fractals, but more generic. So, instead of displaying the whole graph, you only show the most important nodes. When you "zoom in" you show more detail. You probably GeometricZoom? first, then present more information. This is also akin to atmospheric fogging effects plus normal human perception. For example, as one zooms out, one often greeks very small text by representing it in simplified geometric form (usually line segments). The "closer" you get to an object, the more detail you perceive
- DimensionReduction. Information visualization is often tasked with visualization very high dimensional data on two or three dimension displays. One technique is to statistically reduce the number of dimensions in the source data.
- Infosthetics WebLog
- Visual Complexity, a wonderful directory of visualizations
- Robert Horn's Visual Language research
- a taxonomy of Information Visualization Techniques
- Walrus - graph visualisation tool
- Walrus is a graph visualization tool that uses hyperbolic geometry for visualizing large directed graphs, where large is on the order of a million nodes, and about as many links.
- ArgoUML- a java based open source UML tool
- Schie's ConceptMapping? survey
- PingMag? article summarizing some of the most beautiful visualizations.
- Eric Blue's favourite visualizations: http://eric-blue.com/blog/2006/10/dataesthetics_the_power_and_be.html
- the Visual Wiki discusses Robert Horn's Visual language and occasionally information visualization
Jacobson, N. and Bender, W. (1996) Color as a determined communication. IBM Systems Journal, 35(3&4), 526-538. Available from http://www.research.ibm.com/journal/sj/mit/sectiond/jacobson.html
This class (and its assigned readings) may be interesting to some:
[Visualizations in Learning overview] and [syllabus]