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Lecture 13: Graphs and TreesInformation VisualizationCPSC 533C, Fall 2006
Tamara Munzner
UBC Computer Science
24 October 2006
Readings CoveredGraph Visualisation in Information Visualisation: a Survey. IvanHerman, Guy Melancon, M. Scott Marshall. IEEE Transactions onVisualization and Computer Graphics, 6(1), pp. 24-44, 2000.http://citeseer.nj.nec.com/herman00graph.html
Animated Exploration of Graphs with Radial Layout. Ka-Ping Yee,Danyel Fisher, Rachna Dhamija, and Marti Hearst, Proc InfoVis 2001.http://bailando.sims.berkeley.edu/papers/infovis01.htm
SpaceTree: Supporting Exploration in Large Node Link Tree, DesignEvolution and Empirical Evaluation. Catherine Plaisant, JesseGrosjean, and Ben B. Bederson. Proc. InfoVis 2002.ftp://ftp.cs.umd.edu/pub/hcil/Reports-Abstracts-Bibliography/2002-05html/2002-05.pdf
Cushion Treemaps. Jack J. van Wijk and Huub van de Wetering, ProcInfoVis 1999, pp 73-78. http://www.win.tue.nl/∼vanwijk/ctm.pdf
Multiscale Visualization of Small World Networks. David Auber, YvesChiricota, Fabien Jourdan, Guy Melancon, Proc. InfoVis 2003.http://dept-info.labri.fr/∼auber/documents/publi/auberIV03Seattle.pdf
Hermann survey
I true survey, won’t try to summarize here
I nice abstraction work by authorsI Strahler skeletonizationI ghosting, hiding, grouping
Animated Radial Layouts
[Animated Exploration of Graphs with Radial Layout. Ka-Ping Yee,Danyel Fisher, Rachna Dhamija, and Marti Hearst, Proc InfoVis 2001.http://bailando.sims.berkeley.edu/papers/infovis01.htm]
Dynamic Graph Layout
I static radial layouts: known algorithm
I dynamic: little previous workI DynaDAG [North, Graph Drawing 95]I DA-TU [Huang, Graph Drawing 98]
I minimize visual changesI stay true to current dataset structure
I video
AnimationI polar interpolation
I maintain neighbor order
[Animated Exploration of Graphs with Radial Layout. Ka-Ping Yee, Danyel Fisher,Rachna Dhamija, and Marti Hearst, Proc InfoVis 2001.]
More Dynamic Graphs
videoI Dynamic Drawing of Clustered Graphs.
Yaniv Frishman, Ayellet Tal. InfoVis 2004Video Proceedings
SpaceTreeI focus+context tree
I animated transitions
I semantic zooming
I demo
Treemaps
I containment not connection
I difficulties reading
Cushion Treemaps
I show structure with shadingI scale parameter controls global vs. local
Treemap Applications
I cushion treemapsI SequoiaView, Windows appI hard drive usageI http://www.win.tue.nl/sequoiaview/
I popular latelyI http://www.cs.umd.edu/hcil/treemap-history/
Small-World Networks
I high clustering, small path lengthI vs. random uniform distribution
I examplesI social networksI movie actorsI WebI software reverse engineering
I multiscale small-world networksI exploit these properties for better layout
Strength Metric
I strength: contribution to neighborhoodcohesion
I calculate for each edge based onI edge’s POV partition of graph: one, other, both
u vM(u)
M(v)
W(u,v)both
other
one
Strength via CyclesI 3-cycles through (u,v) + 4-cycles through
(u,v)
u vM(u)
M(v)
W(u,v)both
other
one
Cycles: Cohesion Measure
I 3-cycles through u/vI blue + 2 red edges == yellow nodes in both
u vM(u)
M(v)
W(u,v)both
other
one
Cycles: Cohesion Measure
I 3-cycles through u/vI blue + 2 red edges == yellow nodes in both
I all other 3-cycles don’t contain blue u/vedge
I magenta edges impossibleI black, red/green, red/black, etc
impossible
u vM(u)
M(v)
W(u,v)both
other
one
Cycles: Cohesion Measure
I 3-cycles through u/vI blue + 2 red edges == yellow nodes in both
Iexisting
all possible =yellow nodes
all nodes
impossible
u vM(u)
M(v)
W(u,v)both
other
one
Cycles: Cohesion Measure
I 4-cycles through u/vI blue + 2 red + 1 greenI blue + 2 red + 1 cyan
I s(A, B) =existing edges between sets
all possible edges between sets
u vM(u)
M(v)
W(u,v)both
other
one
StrengthI 4-cycles [green edges]
I one-both, other-both, one-otherI s(M(u),W(u,v))+s(M(v),W(u,v))+s(M(u),M(v))
I 4-cycles [cyan edges]I both-bothI s(W(u,v))
I 3-cycles [yellow nodes in both]I |W (u, v)|/(|M(u)| + |M(v)| + |W (u, v)|)
u vM(u)
M(v)
W(u,v)both
other
one
Hierarchical Decomposition
I remove low-strength edgesI maximal disconnected subgraphsI quotient graph: subgraph = higher-level
node
[Multiscale Visualization of Small World Networks. Auber, Chiricota, Jourdan, andMelancon. Proc. InfoVis 2003]
Nested Quotient Graphs
[Multiscale Visualization of Small World Networks. Auber, Chiricota,Jourdan, and Melancon. Proc. InfoVis 2003]
Nested Quotient Graphs
[Multiscale Visualization of Small World Networks. Auber, Chiricota,Jourdan, and Melancon. Proc. InfoVis 2003]
Clustering Quality MetricI automatically determine how many clusters
[Multiscale Visualization of Small World Networks. Auber, Chiricota,Jourdan, and Melancon. Proc. InfoVis 2003]
Critique
I prosI exploit structure of dataI hierarchical structure shown visuallyI automatically determine number of clustersI nifty math
I consI information density could be betterI what if mental model doesn’t match clustering
metric?
Previous: Multilevel Call Matrices Previous: EdgeLens Previous: Visual Access Distortion Previous: H3
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