Graph Visualization and Beyond

1
Graph Visualization and Beyond

• Anne Denton, April 4, 2003
• Including material from a paper by Ivan Herman,
  Guy Melançon, and M. Scott Marshall

2
Outline

• Graph Visualization Part 1 discussed
• Graph drawing and graph visualization
• Graph layout
• Graph Visualization Part 2
• Navigation of large graphs
• Visualization of Node Data
• Glyphs
• New idea combine both
• Graph Visualization Part 2 continued
• Reorganization of data Clustering

3
Navigation and Interaction

• Zoom and pan (discussed previously)
• Geometric zooming
• Semantic zooming
• Clustering
• Fisheye Distortion
• Incremental Exploration and Navigation

4
Focus Context Techniques

• Zooming looses contextual information
• Focus context keeps context
• Example
• Fisheye
• distortion

5
Fisheye Distortion

• Process
• Pick focus point
• Map points within radius using a concave
  monotonic function
• Example Sarkar-Brown distortion function

6
Problem with Fisheye

• Distortion should also be applied to links
• Prohibitively slow (polyline)
• Alternative
• Continue using lines
• Can result in unintended line crossings
• Other Alternative
• Combine layout with focuscontext
• Hyperbolic viewer
• Other combinations possible (e.g. balloon view
  with focus-dependent radii) but not yet done

7
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)

8
Visualization of Node Data??

• So far mostly connectivity
• Exceptions
• Size of files in fly-over
• Color represented stock
• performance in
• http//www.smartmoney.com/marketmap
• Common for data in a spatial context
• Glyphs like weather map symbols
• Tufte has many more suggestions

9
Weather Map Symbols

• Well-known from newspaper weather maps
• Interestingly hard to find on the web!?
• Example below encodes
• 7 items of information in the symbol
• 4 of them graphical
• 2 coordinates by its position on the map

10
Chernoffs Faces

• Assumption
• Humans are
• good at
• processing
• facial
• features

11
Star-Plot

• 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)

12
Idea

• Glyphs for node data
• Connectivity through any of the graph
  visualization tools
• Example
• 5 properties of yeast genes / proteins for arms
• 1 property for color

13
Explanation of Node Information
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Example Nodes

• Important gene
• Essential
• Close to center of chromosome
• Much known
• Relatively long
• (not involved in AHR pathway)
• Pseudo gene
• I.e. no real gene
• change gene
• short

15
Clustering

• Structure-based clustering
• Most common in graph visualization
• Often retain structure of graph
• Useful for user orientation
• Content-based clustering
• Application specific
• Can be used for
• Filtering de-emphasis or removal of elements
  from view
• Search emphasis of an element or group of
  elements

16
Clustering continued

• Common goal
• Finding disjoint clusters
• Clumping
• Finding overlapping clusters
• Common technique
• Least number of edges between neighbors
• (Ratio Cut technique in VLSI design)

17
Hierarchical Clustering

• From successive application
• of clustering process
• Can be navigated
• as tree

18
Visualization of higher levels

• 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

19
Node Metrics

• Measure abstract feature
• Give ranking
• Edge metrics also possible
• Structure-based or content-based
• Examples
• Application-specific weight
• Degree of the node
• Degree of Interest (Furnas)

20
Methods of representing unselected nodes

• Ghosting
• De-emphasizing or
• relegating nodes
• to background
• Hiding
• Not displaying at all
• Grouping
• Grouping under super
• -node representation

21
Summary

• Part 1 showed
• Graph drawing and graph visualization
• Overlap but different goals and problems
• Graph layout Much is known from graph drawing
• Part 2
• Navigation of large graphs
• Key tool in dealing with size
• Reorganization of data Clustering
• Still much to be done
• New Research
• Combine graph visualization with glyph techniques
  for node data