graph visualization and beyond … anne denton, april 4, 2003 including material from a paper by...
TRANSCRIPT
Graph Visualization and Beyond …
Anne Denton, April 4, 2003Including material from a
paper by Ivan Herman, Guy Melançon, and M. Scott
Marshall
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
Navigation and Interaction Zoom and pan (discussed
previously) Geometric zooming Semantic zooming
Clustering
Fisheye Distortion Incremental Exploration and
Navigation
Focus + Context Techniques Zooming looses contextual
information Focus + context keeps context Example
Fisheye distortion
Fisheye Distortion Process
Pick focus point Map points within radius using a concave
monotonic function Example: Sarkar-Brown distortion function
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 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 smallExample not from the paperhttp://touchgraph.sourceforge.net/(Force-directed? Note how animation helps
adjusting to new layout)
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
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
Chernoff’s Faces
Assumption:Humans aregood atprocessingfacialfeatures
Star-Plot Different directions
correspond to different properties
Example: 12 chemical
properities Measured on 53
mineral samples(Hand, Mannila, Smyth,“Principles of DataMining”, MIT Press 2001)
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
Explanation of Node Information
Example Nodes “Important” gene
Essential Close to center of chromosome Much known Relatively long (not involved in AHR pathway)
Pseudo geneI.e. no real gene
“change” gene short
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
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)
Hierarchical Clustering From successive application
of clustering process Can be navigated
as tree
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
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)
Methods of representing unselected nodes Ghosting
De-emphasizing or relegating nodes to background
Hiding Not displaying at all
Grouping Grouping under super
-node representation
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