graph and network visualization
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Graph and NetworkVisualization
John Stasko
Georgia Institute of Technology
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Connections
Connections throughout our lives and the
world Circle of friends
Deltas flight plans
Model connected set as a Graph
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What is a Graph?
Vertices (nodes)
connected by Edges (links)
1 2 30 1 0
1 0 10 1 0
1
23
1: 22: 1, 33: 2 1
32
Adjacency matrix
Adjacency list
Drawing
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Graph Terminology
Graphs can have cycles
Graph edges can be directedorundirected
The degreeof a vertex is the number ofedges connected to it
In-degreeand out-degreefor directed graphs
Graph edges can have values (weights)on them (nominal, ordinal or quantitative)
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Trees are Different
Subcase of general graph
No cycles Typically directed edges
Special designated root vertex
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Graph Uses
In information visualization, any number
of data sets can be modeled as a graph US telephone system
World Wide Web
Distribution network for on-line retailer
Call graph of a large software system
Semantic map in an AI algorithm Set of connected friends
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Graph Visualization Problems
Graph layout and positioning
Make a concrete rendering of abstract graph
Scale
Not too much of a problem for small graphs,but large ones are much tougher
Navigation/Interaction
How to support user changing focus andmoving around the graph
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Layout Algorithms
Entire research communitys focus
Good references: Tutorial (talk slides)
www.cs.brown.edu/people/rt/papers/gd-tutorial/gd-constraints.pdf
G. diBattista, P. Eades, R. Tamassia, and I.Tollis, Graph Drawing: Algorithms for theVisualization of Graphs, Prentice Hall, 1999.
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General GD Information
Good web links www.cs.brown.edu/people/rt/gd.html
www.research.att.com/sw/tools/graphviz/
rw4.cs.uni-sb.de/users/sander/html/gstools.html
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Graph Drawing Conference
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Vertex Issues
Shape
Color
Size
Location Label
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Edge Issues
Color
Size Label
Form Polyline, straight line, orthogonal, grid,
curved, planar, upward/downward, ...
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Aesthetic Considerations
CrossingsCrossings-- minimize towards planar
Total Edge LengthTotal Edge Length-- minimize towards properscale
AreaArea-- minimize towards efficiency
Maximum Edge LengthMaximum Edge Length-- minimize longestedge
Uniform Edge LengthsUniform Edge Lengths-- minimize variances
Total BendsTotal Bends-- minimize orthogonal towardsstraight-line
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Which Matters?
Various studies examined which of the
aesthetic factors matter most and/or whatkinds of layout/vis techniques look best Purchase, Graph Drawing 97
Ware et al, Info Vis1(2), June 02
Ghoniem et al, Info Vis 4(2), Summer 05
Results mixed: Edge crossings seemimportant
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Layout Heuristics
Layout algorithms can be
planar
grid-based
orthogonal
curved lines
hierarchies
circular ...
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Types of Layout Algorithms
From:P. Mutzel, et al
Graph Drawing 97
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Layout Examples
Cool java applethttp://java.sun.com/applets/jdk/1.2/demo/applets/GraphLayout/example1.html
Examples of dynamic graph layoutalgorithms
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Scale Challenge
May run out of space for vertices and
edges (turns into ball of string) Can really slow down algorithm
Often use clusteringto help
Extract highly connected sets of vertices
Collapse some vertices together
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Navigation/Interaction Issues
How do we allow a user to query, visit, or
move around a graph? Changing focus may entail a different
rendering
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Graph/Network Visualization
One of the oldest and most studied areas
of information visualization
Good overview
Herman et al, IEEE TVCG 00
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Examples www.visualcomplexity.com
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Graph Uses
Facilitate understanding of complex socio-
economic patterns Social Science visualization gallery (Lothar
Krempel):
http://www.mpi-fg-koeln.mpg.de/~lk/netvis.html
Next slides: Krempel & Plumpers studyof World Trade between OECD countries,1981 and 1992
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1981http://www.mpi-fg-koeln.mpg.de/~lk/netvis/trade/WorldTrade.html
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1992
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Social Network Visualization
Social Network Analysis (Linton Freeman)
http://www.sfu.ca/~insna
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People connections
Charles Isbell, Cobot
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Vizster
Saw in Social Visualization lecture
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SocialAction
Perer & Shneiderman
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Graph Uses
Facilitate understanding of network flows,
relations
Even information with a geographical
content can best appear as a network railmaps
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3 Subway Diagrams
Geographic landmarks largely suppressed
on maps, except water (rivers in Paris,London) and asphalt (highways in Atlanta)
Rather fitting, no?
These are more graphsgraphsthan maps!
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Case Study
SeeNet
Visualizing network data (phone traffic)R. Becker, S. Eick and A. WilksAT&T
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Domain
AT&T long distance phone network
110 Nodes (switches)Geographical location
Connected by 12,000 links
Directed, almost completely connected
Data every 5 minutes
EARTHQUAKE!!!EARTHQUAKE!!!
Oct. 17, 1989
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Questions
Where are the overloads?
Which links are carrying most traffic? Was there network damage?
Is there underutilized capacity?
Are calls getting in to affected area or arethere bottlenecks?
Is overload increasing or decreasing?
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Edge Drawing Strategies
116Label
Thickness
Color
116
29Directed
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Problems
Too many lines!
Occlusion
Long lines become more important
Cant see what happens in Midwest
Solutions Use half/half technique out/out
Draw most important last Use thickness & color for traffic
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Earthquake data
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More Help
Shorten all lines so as to de-emphasize
transcontinental links
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Case Study
NicheWorks
Interactive Visualization of Very Large GraphsGraham WillsLucent (at time)
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Big Graphs
20,000 - 1,000,000 Nodes
Works well with 50,000 Projects
Software Engineering
Web site analysis
Large database correlation
Telephone fraud detection
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Features
Typical interactive operations
Sophisticated graph layout algorithm 3 Layouts
Circular
Hexagonal
Tree
3 Incremental AlgorithmsSteepest Descent
Swapping
Repelling
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Web Site Example
Circle layout Hexagonal layout Tree layout
I t f
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Interface
I t f
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Interface
Ph F d E l
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Phone Fraud Example
40,000 calls35,000 callers
Shown arepeople callingthat country
Length of
edge isduration ofcall
F d E l
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Fraud Example
Filtering for peoplewho made multiplecalls and spent asignificant amountof time on the phone
Playing with parameters
like these is importantbecause fraudstersknow how to evade
Note the two peoplecalling Israel and Jordan
F d E l
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Fraud Example
Zooming in, we notice they havesimilar calling patterns and numbers
(likely part of same operation)
Illegal to call between Israel andJordan at the time, so fraudsters
set up rented apts in US and chargeIsraeli and Jordanian business peoplefor 3rd party calling
When bills came to US, they would
ignore and move on
M N t St ff
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More Neat Stuff
http://willsfamily.org/gwills/
Lots of interesting application areas More details on NicheWorks
Oth A li ti
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Other Applications
Email
How would you visualize all email traffic inyour department between pairs of people?
Solutions???
Sol tions
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Solutions
Put everyone on circle, lines between
Color or thicken line to indicate magnitude Use spring/tension model
People who send a lot to each other are
drawn close together Shows clusters of communications
More Email
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More Email
How about visualizing internet traffic?
Byte traffic into the ANS/NSFnet T3 backbone for the month of November, 1993
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http://www.ncsa.uiuc.edu/SCMS/DigLib/text/technology/Visualization-Study-NSFNET-Cox.html
Inbound traffic measured in billions of bytes on the NSFNET T1 backbone for September 1991
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Linux kernel
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Linux kernel
http://perso.wanadoo.fr/pascal.brisset/kernel3d/kernel3d.html
TouchGraph
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TouchGraph
www.touchgraph.com
Focus of Graph
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Focus of Graph
Particular node may be focus, oftenplaced in center for circular layout
How does one build an interactive systemthat allows changes in focus?
Use animation
Intuition about changes not always right
Focus Change Animation
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Focus Change Animation
Straight linear interpolation
of focus changes not asappealing as changes alongpolar coordinates
Yee, Fisher, Dhamija, HearstInfoVis 01
Video
MoireGraphs
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MoireGraphs
Combine a focus + context radial graphlayout with a variety of interactiontechniques
Useful when visual information such as
images is also present at each node andmust be displayed
Jankun-Kelly & MaInfoVis 03
Navigation and interaction
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Navigation and interaction
Video
Alternative Approaches
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Alternative Approaches
Recently, researchers have been exploringapproaches other than the traditionalnode-link views
Adjacency Matrices
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Adjacency Matrices
MatrixExplorer
Henry & Fekete
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PivotGraph
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PivotGraph
Cluster nodes on an attribute valueShow links between sets Wattenberg
CHI 06
Semantic Substrates
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Semantic Substrates
Shneiderman & Aris
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Separate nodes by attributeUse dynamic querycapabilities
More Resources
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Network visualization resources http://www.caida.org/projects/internetatlas/viz/
Good article on graph layout http://www.csi.uottawa.ca/ordal/papers/sander/main.html
More to Come...
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Topic of WWW/InfoSphere (next lecture)will touch on graphs and networks too
Lots of example visualizations
End
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References
Spence and CMS texts
All referred to papers and web sites
Dagon and Leahy, F 99 slides