1. Visualization of Content Information in Networks using GlyphNet
Anne Denton and Paul Juell
Department of Computer Science
North Dakota State University
Fargo, ND, USA

2. Information Visualization on a Graph
Genomics
Protein-protein interactions
Biochemical pathways
WWW
Link structure
Scientific publications
Citations
Social networks
Scientific American 05/03

3. Graph/Tree Visualization Tools
Euler 1736
Since then
Many layouts
Aesthetic rules
Navigation
Probe capability

4. Navigation / Probe Capability

5. Visual Data Mining as Hypothesis Generation Process
Hypotheses regarding
Visualization tools
Edge distribution
Relationship between node content and edge distribution
Relationship between node content of different nodes:
Mining relational data ?
Graph visualization tool
~~~~
Probe capability
GlyphNet

6. Visualization of Node Data??
So far mostly connectivity
Exceptions (SGI Filesystem browser)
Color
Size
Glyphs in Clustering
P. Eades, Q. Feng, “Multilevel Visualization of Clustered Graphs,” Lecture Notes in Computer Science”, 1190, pp , 1997

7. Glyphs Weather map symbols Chernoff faces
Chernoff, H., “The use of faces to represent points in k-dimensional space graphically,”  Journal of the American Statistical Association, Vol. 68, pp , 1973.

8. Adapting a Star Plot Star plot Our solution
Star with n arms for n attributes
Value: distance from center
Connect points
Our solution
Embed in circle
Filled pie slices

9. Bioinformatics Example
Motivated by KDD-cup 02 and other bioinformatics problems
Graph: Protein-protein interactions in yeast
From experiments
Undirected graph
Categorical and continuous attributes
Essential (organism survives gene-deletion experiment
Color
Red: AHR
Green: not AHR
Yellow: “control”
*AHR: Aryl Hydrocarbon Receptor Signaling Pathway

10. Questions Prediction of red (AHR) What are interesting patterns?
Which attributes in neighbors are relevant?
How should we integrate neighbor knowledge?
What are interesting patterns?
Which properties say more about neighboring nodes than about the node itself?
But not:

11. Integration of Results into Other Data Mining Algorithms
Include additional attribute for each object
Count-based: Number of neighbors with property “essential” (example: 2)
Truth-value-based: Existence of a neighbor with property “essential” (example: true)

12. Summary of GlyphNet Idea: Visualization of node data as glyph
Goal: Identify patterns that involve multiple nodes
Next step: Validate pattern numerically
Possible use: Include in node-based data mining algorithm