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Graph Visualization and
Beyond …
Anne Denton, April 4, 2003
Including material from a paper
by Ivan Herman, Guy Melançon,
and M. Scott Marshall
Outline
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Graph Visualization Part 1 discussed
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Graph Visualization Part 2
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Navigation of large graphs
Visualization of Node Data
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Graph drawing and graph visualization
Graph layout
Glyphs
New idea: combine both
Graph Visualization Part 2 continued
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Reorganization of data: Clustering
Navigation and Interaction
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Zoom and pan (discussed previously)
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Geometric zooming
Semantic zooming
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Clustering
Fisheye Distortion
Incremental Exploration and Navigation
Focus + Context Techniques
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Zooming looses contextual information
Focus + context keeps context
Example
Fisheye
distortion
Fisheye Distortion
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Process
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Pick focus point
Map points within radius using a concave
monotonic function
Example: Sarkar-Brown distortion function
Problem with Fisheye
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Distortion should also be applied to links
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Alternative
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Prohibitively slow (polyline)
Continue using lines
Can result in unintended line crossings
Other Alternative
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Combine layout with focus+context
Hyperbolic viewer
Other combinations possible (e.g. balloon view
with focus-dependent radii) but not yet done
Incremental Exploration and
Navigation
For very large graphs (e.g. Internet)
 Small portion displayed
 Other parts displayed as needed
 Displayed graph small
 Layout and interaction times may be small
Example not from the paper
http://touchgraph.sourceforge.net/
(Force-directed? Note how animation helps
adjusting to new layout)
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Visualization of Node Data??
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So far mostly connectivity
Exceptions:
Size of files in fly-over
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Color represented stock
performance in
http://www.smartmoney.com/marketmap
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Common for data in a spatial context
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Glyphs like weather map symbols
Tufte has many more suggestions
Weather Map Symbols
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Well-known from newspaper weather maps
Interestingly: hard to find on the web!?
Example below encodes
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7 items of information in the symbol
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4 of them graphical
2 coordinates by its position on the map
Chernoff’s Faces
Assumption:
Humans are
good at
processing
facial
features
Star-Plot
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Different directions
correspond to
different properties
Example:
12 chemical
properities
 Measured on 53
mineral samples
(Hand, Mannila, Smyth,
“Principles of Data
Mining”, MIT Press 2001)
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Idea
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Glyphs for node data
Connectivity through
any of the graph
visualization tools
Example:
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5 properties of yeast
genes / proteins for
arms
1 property for color
Explanation of Node
Information
Example Nodes
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“Important” gene
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Essential
Close to center of chromosome
Much known
Relatively long
(not involved in AHR pathway)
Pseudo gene
I.e. no real gene
“change” gene
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short
Clustering
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Structure-based clustering
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Most common in graph visualization
Often retain structure of graph
Useful for user orientation
Content-based clustering
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Application specific
Can be used for
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Filtering: de-emphasis or removal of elements from view
Search: emphasis of an element or group of elements
Clustering continued
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Common goal
 Finding disjoint clusters
Clumping
 Finding overlapping clusters
Common technique
 Least number of edges between neighbors
(Ratio Cut technique in VLSI design)
Hierarchical Clustering
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From successive application
of clustering process
Can be navigated
as tree
Visualization of higher levels
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Herman et al. say
glyphs are used (?)
P. Eades, Q. Feng, “Multilevel
Visualization of Clustered Graphs,
” Lecture Notes in Computer
Science”, 1190, pp 101-112,
1997
Node Metrics
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Measure abstract feature
Give ranking
Edge metrics also possible
Structure-based or content-based
Examples
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Application-specific weight
Degree of the node
“Degree of Interest” (Furnas)
Methods of representing
unselected nodes
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Ghosting
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Hiding
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De-emphasizing or
relegating nodes
to background
Not displaying at all
Grouping
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Grouping under super
-node representation
Summary
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Part 1 showed
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Graph drawing and graph visualization
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Graph layout: Much is known from graph drawing
Part 2
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Navigation of large graphs
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Key tool in dealing with size
Reorganization of data: Clustering
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Overlap but different goals and problems
Still much to be done
New Research
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Combine graph visualization with glyph techniques
for node data