Graph Visualization: Extensions

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Presented byDave FuhryYang Zhang

Outline

• Some Visualization Tools• Why visualization? (Re-motivation)• Challenges• Information Visualization Data Types• TreeMaps• Handling high dimension

– PCA, Co-Clustering, Parallel Coordinates, Grand Tour• PRISM-HD: APSS plot, CSV• Applications 1: Disaster (Geodesic, content)• Applications 2: Social Network Analysis

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Some Visualization Tools

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Gephi

Prefuse Gnuplot

GraphViz

matplotlib

NodeXLPajek

d3

Sigma.jsCobweb

InfoViz

Cytoscape

Guess

NetworkX

Force-Directed Graph

Interactive

GUI

Weka

Orange

Outline

• Same challenges as with graph layout• Layout

– Representing items, their attributes, and structure.• Scale

– “Pixel wall”, but Big Data scales to billions of records.– Shneiderman ’08: Billion records into a Million pixels

• Interaction– Enable user to explore and get insight

Set A Set B Set C Set DX Y X Y X Y X Y

10 8.04 10 9.14 10 7.46 8 6.588 6.95 8 8.14 8 6.77 8 5.76

13 7.58 13 8.74 13 12.74 8 7.719 8.81 9 8.77 9 7.11 8 8.84

11 8.33 11 9.26 11 7.81 8 8.4714 9.96 14 8.1 14 8.84 8 7.04

6 7.24 6 6.13 6 6.08 8 5.254 4.26 4 3.1 4 5.39 19 12.5

12 10.84 12 9.11 12 8.15 8 5.567 4.82 7 7.26 7 6.42 8 7.915 5.68 5 4.74 5 5.73 8 6.89

[Anscombe 73]

Summary Statistics Linear RegressionuX = 9.0 σX = 3.317 Y2 = 3 + 0.5 XuY = 7.5 σY = 2.03 R2 = 0.67

Slides courtesy: Jeffrey Heer @ Stanford: A Brief Introduction to Data Visualization

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Set A

Set C Set D

Set B

X X

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Y

Slides courtesy: Jeffrey Heer @ Stanford: A Brief Introduction to Data Visualization

Slide courtesy: Ben Shneiderman @ UMD: Information Visualization for Knowledge Discovery.

Wattenberg 1998

http://www.smartmoney.com/map-of-the-market/

[Shneiderman ‘92]

Wattenberg 1998

rectangle size: market cap (Q)rectangle position: market sector (N), market cap (Q)color hue: loss vs. gain (N, O)color value: magnitude of loss or gain (Q)

Dimensionality Reduction

• Multidimensional scaling, e.g. PCA

• Self-organizing mapImage credit: Matthias Scholz, http://www.nlpca.org/

Parallel Coordinates

• Draw vertical line for each dimension• Item drawn as line through dimensions

Figures from Xiang, Fuhry, Jin, Zhao, Huang:Visualizing Clusters in Parallel Coordinates…, PAKDD ‘12

Grand Tour

• Visualize HDD with 2D scatterplots• “Tour” randomly generated planes• Smooth transition

[Asimov ‘83][Buja, Cook, Asimov, Hurley. ‘04]

Grand Tour (Demo)

Projection of a grand tour of six-dimensional data. Source: GGobi software.

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Social networks Protein Interactions Internet

VLSI networks Data dependenciesNeighborhood graphs

PRISM-HD• What?

– A novel mechanism for exploring complex data

• Why?– User is often overwhelmed with

characteristics of data– Befuddled on where to start

• How?– Given, similarity measure-of-interest– Compute similarity graph at threshold (t)

• Key: Graphs are dimensionless

– Provide user graph visualization cues• User determines next threshold and

repeats

HD

HD

HIGH THRESHOLD MODERATE THRESHOLD LOW THRESHOLD

Applications 1: Disaster Mgmt / Geodesic Overlays

Applications 2:Disaster Mgmt / Community Analysis

[Fuhry, Ruan, and Parthasarathy. WebSci’12]

Applications 3:Social Network Analysis

Applications 3:Social Network Analysis (2)

Appendix

Nominal, Ordinal and QuantitativeN - Nominal (labels)

– Fruits: Apples, oranges, …

O - Ordinal (rank-ordered)– Quality of meat: Grade A, AA, AAA

Q - Interval (location of zero arbitrary)– Dates: Jan, 19, 2006; Location: (LAT 33.98, LONG -118.45)– Like a geometric point. Cannot compare directly– Only differences (i.e. intervals) may be compared

Q - Ratio (zero fixed)– Physical measurement: Length, Mass, Temp, …– Counts and amounts– Like a geometric vector, origin is meaningful

S. S. Stevens, On the theory of scales of measurements, 1946

Slide courtesy: Jeffrey Heer @ Stanford: A Brief Introduction to Data Visualization

Ag

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Marital Status

Sin

gle

Marr

ied

Div

orc

e dW

idow

e d

19701980

19902000

Year

0-19

20-39

40-59

60+

All Marital Status

All Ages

All Years

Sum along Marital Status

Sum along Age

Sum along Year

Slide courtesy: Jeffrey Heer @ Stanford: A Brief Introduction to Data Visualization

Position (x 2)SizeValueTextureColorOrientationShape

Visual encoding variables

PositionLengthAreaVolumeValueTextureColorOrientationShapeTransparencyBlur / Focus …

Visual encoding variables

Image courtesy: “Jer” of blprnt.com. “Just Landed”