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Visualization of Content
Information in Networks
using GlyphNet
Anne Denton and Paul Juell
Department of Computer Science
North Dakota State University
Fargo, ND, USA
Information Visualization on a Graph
 Genomics
 Protein-protein
interactions
 Biochemical
pathways
 WWW
 Link structure
 Scientific publications
 Citations
 Social networks
Scientific American 05/03
Graph/Tree Visualization Tools
 Euler 1736
 Since then
 Many layouts
 Aesthetic rules
 Navigation
 Probe capability
Navigation / Probe Capability
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
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Probe
capability
GlyphNet
Visualization of Node Data??
 So far mostly
connectivity
 Exceptions (SGI
Filesystem
browser)
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Color
Size
 Glyphs in
Clustering
P. Eades, Q. Feng, “Multilevel Visualization of
Clustered Graphs,” Lecture Notes in
Computer Science”, 1190, pp 101-112, 1997
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. 361-368, 1973.
Adapting a Star Plot
 Star plot
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Star with n arms for n
attributes
Value: distance from
center
Connect points
 Our solution
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Embed in circle
Filled pie slices
Bioinformatics Example

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Motivated by KDD-cup 02
and other bioinformatics
problems
Graph: Protein-protein
interactions in yeast

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Categorical and continuous
attributes
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From experiments
Undirected graph
Essential (organism survives
gene-deletion experiment
Color
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Red: AHR
Green: not AHR
Yellow: “control”
*AHR: Aryl Hydrocarbon Receptor Signaling Pathway
Questions
 Prediction of red (AHR)
 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:
Integration of Results into Other Data
Mining Algorithms
 Include additional
attribute for each object

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Count-based: Number
of neighbors with
property “essential”
(example: 2)
Truth-value-based:
Existence of a neighbor
with property “essential”
(example: true)
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