Graph Visualization and Navigation in Information Visualization: a Survey - PowerPoint PPT Presentation

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Graph Visualization and Navigation in Information Visualization: a Survey

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Title: Graph Visualization and Navigation in Information Visualization: a Survey


1
Graph Visualization and Navigation in Information
Visualization a Survey
  • Ivan Herman, Guy Melançon, and M. Scott Marshall
  • (Presentation Anne Denton
  • March 6, 2003)

2
Outline
  • Graph drawing and graph visualization
  • Graph layout
  • Navigation of large graphs
  • Reorganization of data Clustering

3
Information Visualizationvs. Graph Drawing
  • Graph Drawing
  • Old topic, many books, etc.
  • May have other goals than visualization
  • E.g. VLSI design
  • Graph Visualization
  • Size key issue
  • Usability requires nodes to be discernable
  • Navigation considered

4
Node Information?
  • Sometimes a size or importance is represented
  • Navigational systems may have links to data
  • Glyphs?
  • Mentioned as representation of higher levels in
    hierarchical clustering
  • Focus on structure-based properties
  • Application independent

5
Examples
  • Class browsers
  • Entity relationship diagrams
  • Real-time systems (state transition diagrams)
  • VLSI circuit design (circuit schematics rather
    than actual design)
  • Document management system
  • Web-navigation
  • Virtual Reality (scene graph)

6
History of Graph Drawing
  • Euler used a drawing to solve the Königsberger
    Brückenproblem (1736)
  • Symposia on Graph Drawing initiated 1992
  • Issues
  • Planarity
  • No edges cross in 2D
  • Aesthetic rules
  • Edges should have same length
  • Edges should be straight lines
  • Isomorphic substructures displayed equivalently

7
Reingold and Tilford algorithm for Trees
  • Note Isomorphic subtrees laid out in same way
  • Problem High Density of nodes

8
Tasks Related to Graph Drawing
  • Layering a graph
  • Turning graph into directed acyclic graph
  • Planarizing (achieve that no edges cross)
  • Minimizing area
  • Minimizing number of bends in edges
  • But
  • Algorithms too complex for large graphs

9
Problem Size
  • Previous example few hundred nodes
  • How about thousands of nodes?
  • Solutions
  • 3D
  • Non-Euclidean geometry (e.g., hyperbolic
    geometry)
  • Reduce size
  • Show part only / blow up part

10
Other problems related to Navigation
  • Predictability
  • Two different runs on similar trees should lead
    to similar results
  • Traditional layouts next page are predicatable
  • Time Complexity
  • Real time interaction

11
Traditional Tree Layouts
  • Classical layout on earlier slide
  • H-tree layout best for balanced trees
  • Radial view
  • Balloon view related to 3-d cone tree

12
Tree-Map
  • Useful for information visualization because area
    is meaningful
  • Example http//www.smartmoney.com/marketmap
  • Size represents market share
  • Color represents performance
  • More information available through clicking
  • Problem Tree structure less clear

13
Layout of Directed Graphs
  • Layering (http//www.csus,yk,ue/staff/NikolaNikolo
    v/phd)

14
Spring Layout
  • Force directed
  • Nodes are modeled as physical bodies that are
    connected through springs (edges)
  • High time complexity gt O(N3)
  • Not predictable

15
Spanning Trees
  • Further conclusions from size
  • Dont insist on planarity
  • Dont worry about edge crossings
  • Graph can be visualized through minimum spanning
    tree
  • Additional edges added later
  • Very common technique
  • Helps with predictability
  • Visualization depends on starting point

16
3D Techniques
  • Benefits
  • Gaining more space
  • Human familiarity with 3D
  • Problems
  • 2D displays
  • Missing motion and stereo cues
  • May be solved by better technology

17
Examples of 3D Techniques
  • 3D version of a radial tree
  • Info cube

18
Cone Tree
  • Developed directly for 3D
  • Interactiveness important
  • Nodes can be rotated

19
Fly-Through of 2D Representation
  • SGI File System Navigator
  • Size represents file size
  • Similar
  • Perspective
  • wall

20
Hyperbolic Layout
  • Mainly used for trees
  • E.g. web-content viewers
  • 2D or 3D
  • Similar to fish-eye lense
  • Possibility of interacting with large trees

21
EBI Hyperbolic Viewers
  • 2D example applets
  • http//industry.ebi.ac.uk/alan/components/example
    s/example1.html
  • http//www.inxight.com/map
  • 3D image

22
Hyperbolic Viewer Concepts
  • For a given point and non-intersecting line many
    parallel lines through point
  • Segments that are congruent in the hyperbolic
    sense are exponentially smaller in the Euclidean
    sense when approaching the perimeter
  • Projective Klein model
  • Straight lines
  • Suitable for 4x4 matrix-based graphics
  • Conformal or Poincaré model
  • Straight lines drawn as arcs
  • Angles are drawn correctly in Euclidean sense
  • Computationally more demanding

23
Klein Model vs. Poincare Model
  • T. Munzner, P. Burchard, Visualizing the
    structure of the World Wide Web in 3D Hyperbolic
    Space, Proceedings of the VRML Symposium, pp
    33-38, 1995.
  • Klein Model Poincare Model

24
Simple Tree Construction Algorithm
  • Node P with with wedge QPR
  • Subtrees start at P1, P2, and P3
  • Euclidean Hyperbolic

25
Navigation and Interaction
  • Zoom and pan
  • Zoom for graphs exact, not pixel-based
    (adjustment of screen transformations)
  • Geometric zooming
  • Simple blow-up
  • Semantic zooming
  • Content changes
  • Clustering

26
Problem with Combination of Zoom and Pan
  • Assume zoom and pan independent
  • Objects may
  • temporarily
  • move away
  • Solution Space-
  • scale diagram
  • (Semantic zoom
  • picture differs
  • for each level)

27
Focus Context Techniques
  • Zooming looses contextual information
  • Focus context keeps context
  • Example
  • Fisheye
  • distortion

28
Fisheye Distortion
  • Process
  • Pick focus point
  • Map points within radius using a concave
    monotonic function
  • Example Sarkar-Brown distortion function

29
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

30
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)

31
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

32
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)

33
Hierarchical Clustering
  • From successive application
  • of clustering process
  • Can be navigated
  • as tree

34
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

35
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)

36
Methods of representing unselected nodes
  • Ghosting
  • De-emphasizing or
  • relegating nodes
  • to background
  • Hiding
  • Not displaying at all
  • Grouping
  • Grouping under super
  • -node representation

37
Summary
  • Graph drawing and graph visualization
  • Overlap but different goals and problems
  • Graph layout
  • Much is known from graph drawing
  • Navigation of large graphs
  • Key tool in dealing with size
  • Reorganization of data Clustering
  • Still much to be done
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