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