Improving the Readability of
Network Visualizations
Cody Dunne
cdunne@cs.umd.edu
FDA OBI – April 8, 2013
Why Visualization?
Anscombe’s Quartet
I
x
II
y
x
III
y
x
IV
y
x
y
10.00
8.04
10.00
9.14
10.00
7.46
8.00
6.58
8.00
6.95
8.00
8.14
8.00
6.77
8.00
5.76
13.00
7.58
13.00
8.74
13.00
12.74
8.00
7.71
9.00
8.81
9.00
8.77
9.00
7.11
8.00
8.84
11.00
8.33
11.00
9.26
11.00
7.81
8.00
8.47
14.00
9.96
14.00
8.10
14.00
8.84
8.00
7.04
6.00
7.24
6.00
6.13
6.00
6.08
8.00
5.25
4.00
4.26
4.00
3.10
4.00
5.39
19.00
12.50
12.00
10.84
12.00
9.13
12.00
8.15
8.00
5.56
7.00
4.82
7.00
7.26
7.00
6.42
8.00
7.91
5.00
5.68
5.00
4.74
5.00
5.73
8.00
6.89
Anscombe’s Quartet - Statistics
Property
Value
Equality
Mean of x in each case
9
Exact
Variance of x in each case
11
Exact
Mean of y in each case
7.50
To 2 decimal places
Variance of y in each case
4.122 or 4.127
To 3 decimal places
Correlation between x and
0.816
y in each case
Linear regression line in
each case
To 3 decimal places
To 2 and 3 decimal
y = 3.00 + 0.500x
places, respectively
Anscombe’s Quartet - Scatterplots
Networks!
Tweets of the #Win09 Workshop
Who Uses Network Analysis
Sociology
Scientometrics
Biology
Urban
Planning
Politics
Archaeology
WWW
NodeXL
Collect data, Excel analysis, statistics, visualization, layout
algorithms, filtering, clustering, attribute mapping…
NodeXL Graph Gallery
smrfoundation.org
NodeXL as a Teaching Tool
I. Getting Started with Analyzing Social Media Networks
1. Introduction to Social Media and Social Networks
2. Social media: New Technologies of Collaboration
3. Social Network Analysis
II. NodeXL Tutorial: Learning by Doing
4. Layout, Visual Design & Labeling
5. Calculating & Visualizing Network Metrics
6. Preparing Data & Filtering
7. Clustering &Grouping
III Social Media Network Analysis Case Studies
8. Email
9. Threaded Networks
10. Twitter
11. Facebook
12. WWW
13. Flickr
14. YouTube
15. Wiki Networks
http://www.elsevier.com/wps/find/bookdescription.cws_home/723354/description
NodeXL as a Research Tool
Bonsignore EM, Dunne C, Rotman D, Smith M, Capone T, Hansen DL and Shneiderman B (2009), "First
steps to NetViz Nirvana: Evaluating social network analysis with NodeXL", In CSE '09. pp. 332-339.
DOI:10.1109/CSE.2009.120
Mohammad S, Dunne C and Dorr B (2009), "Generating high-coverage semantic orientation lexicons from
overtly marked words and a thesaurus", In EMNLP '09. pp. 599-608.
Smith M, Shneiderman B, Milic-Frayling N, Rodrigues EM, Barash V, Dunne C, Capone T, Perer A and Gleave
E (2009), "Analyzing (social media) networks with NodeXL", In C&T '09. pp. 255-264.
DOI:0.1145/1556460.1556497
Outline
Motif
Simplification
Group-in-a-Box
Layouts
Sample of Other
Projects
Lostpedia articles
Observations
1: There are repeating patterns in
networks (motifs)
2: Motifs often dominate the
visualization
3: Motifs members can be
functionally equivalent
Motif Simplification
Fan Motif
2-Connector Motif
Lostpedia articles
Lostpedia articles
Glyph Design: Fan
Glyph Design: Connector
Cliques too!
Interactivity
Fan motif: 133 leaf vertices
with head vertex “Theory”
Senate Co-Voting: 65%
Agreement
Senate Co-Voting: 70%
Agreement
Senate Co-Voting: 80%
Agreement
Discussion
• Motif simplification effective for
• Reducing complexity
• Understanding larger or hidden relationships
• Qualitative and task-based user studies
• Available now in NodeXL: nodexl.codeplex.com
Dunne C and Shneiderman B (2013), "Motif simplification: improving network visualization readability with
fan, connector, and clique glyphs", In CHI '13.
Shneiderman B and Dunne C (2012), "Interactive network exploration to derive insights: Filtering, clustering,
grouping, and simplification", In Graph Drawing ‘12. pp. 2-18. DOI:10.1007/978-3-642-36763-2_2
Outline
Motif
Simplification
Group-in-a-Box
Layouts
Sample of Other
Projects
Group-in-a-Box Meta-Layouts
• Squarified Treemap
• Croissant-Donut
• Force-Directed
Risk Movements
Plain Layout
with Clusters
Risk Movements
GIB Treemap
Risk Movements
GIB Croissant
Risk Movements
GIB Force-Directed
Pennsylvania
Innovation
Pennsylvania
Innovation
GIB Treemap
Discussion
• Three Group-in-a-Box layouts for dissecting networks
• Improved group and overview visualization
• Tradeoffs: Filling space vs. showing relationships
• Empirical Twitter evaluation
• Available now/soon in NodeXL: nodexl.codeplex.com
Shneiderman B and Dunne C (2012), "Interactive network exploration to derive insights: Filtering, clustering,
grouping, and simplification", In Graph Drawing ‘12. pp. 2-18. DOI:10.1007/978-3-642-36763-2_2
Chaturvedi S, Ashktorab Z, Dunne C, Zacharia R, and Shneiderman B (2013), "Group-in-a-Box layouts for
visualizing network communities and their ties", Under submission.
Rodrigues EM, Milic-Frayling N, Smith M, Shneiderman B, and Hansen (2011), “Group-in-a-Box layout for
multi-faceted analysis of communities”, In SocialCom ’11. pp. 354-361.
DOI:10.1109/PASSAT/SocialCom.2011.139
Outline
Motif
Simplification
Group-in-a-Box
Layouts
Sample of Other
Projects
Performance Improvement
• Nvidia CUDA 240-core
• Fruchterman-Reingold
• Up to 802X speedup
• Eigenvector Centrality
• Up to 17,972X speedup
Sharma P, Khurana U, Shneiderman B, Scharrenbroich M, and Locke J (2011), Speeding up network layout
and centrality measures for social computing goals. In SBP '11. pp. 244-251. DOI:10.1007/978-3-642-196560_35
Evolving Twitter Topics
Malik S, Smith A, Papadatos P, Li J, Dunne C, and Shneiderman B. “TopicFlow: Visualizing topic alignment of
Twitter data over time”. Under submission.
Evolving Network Attributes
Gove R, Gramsky N, Kirby R, Sefer E, Sopan A, Dunne C, Shneiderman B and Taieb-Maimon M (2011),
"NetVisia: Heat map & matrix visualization of dynamic social network statistics & content", In SocialCom '11.
pp. 19-26. DOI:10.1109/PASSAT/SocialCom.2011.216
Document Collections
Dunne C, Shneiderman B, Gove R, Klavans J and Dorr B (2012), "Rapid understanding of scientific paper
collections: Integrating statistics, text analytics, and visualization", JASIST. Vol. 63(12). pp. 2351-2369.
Gove R, Dunne C, Shneiderman B, Klavans J and Dorr B (2011), "Evaluating visual and statistical exploration
of scientific literature networks", In VL/HCC '11. pp. 217-224.
Exposing Exploration History
Dunne C, Riche NH, Lee B, Metoyer RA and Robertson GG (2012), "GraphTrail: Analyzing large multivariate,
heterogeneous networks while supporting exploration history", In CHI '12. pp. 1663-1672.
Riche N, Lee B and Dunne C (2011), "Interactive visualization for exploring multi-modal, multi-relational, and
multivariate graph data". U.S. Patent Application No. (13/041474).
Funders & Collaborators
Funding
• NSF grants SBE 0915645, IIS 0705832, IIS 0968521
• HHS SHARP grant 10510592
• Social Media Research Foundation, Connected Action Consulting Group,
Microsoft External Research, Microsoft Research, National Cancer
Institute
Co-Authors
• Ben Shneiderman, Marc Smith, Snigdha Chaturvedi, Zahra Ashktorab,
Rajan Zacharia, Tony Capone, Eduarda Mendes Rodrigues, Natasa MilicFrayling, Nathalie Riche, Bongshin Lee, Ron Metoyer, George Robertson,
Robert Gove, Bonnie Dorr, Judith Klavans, Saif Mohammad, Puneet
Sharma, Ping Wang, Awalin Sopan, Nick Gramsky, Rose Kirby, Emre Sefer,
Meirav Taieb-Maimon, Vladimir Barash, Adam Perer, Eric Gleave, Derek
Hansen, Elizabeth Bonsignore, Dana Rotman, Ryan Blue, Adam Fuchs, Kyle
King, and Aaron Schulman
Collaborators
• Catherine Plaisant, Jon Froehlich, Leah Findlater, Yiyan Liu
Discussion
Effective node-link visualizations in NodeXL:
• Motif simplification to reduce complexity
• Group-in-a-Box Layouts to show groups and ties
Additional projects targeted at specific use cases
• Twitter, evolving networks, document collections,
shared explorations
Cody Dunne
cdunne@cs.umd.edu
www.cs.umd.edu/~cdunne/