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Network Visualization in NodeXL
Cody Dunne
IBM Research – Cambridge, MA
cdunne@us.ibm.com
Boston Data Swap Skill-A-Thon
Oc...
The Data
Problem

2
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...
Anscombe’s Quartet - Statistics
Property

Value

Equality

Mean of x in each case

9

Exact

Variance of x in each case

1...
Anscombe’s Quartet - Scatterplots

5
No catalogue of techniques can convey a
willingness to look for what can be seen, whether
or not anticipated. Yet this is ...
Node-Link Network Visualization

Node 1

Node 2

Alice

Bob

Alice

Cathy

Cathy

Alice

7
Tweets of the #Win09 Workshop
#

User 1

User 2

#

User 1

User 2

1 20andlife

barrywellman

15 danevans87

informor

2 ...
Tweets of the #Win09 Workshop

9
Who Uses Network Analysis

Sociology

Scientometrics

Biology

Urban
Planning

Politics

Archaeology

WWW
Network visualization is highly useful, but hard!

There are many ways to make it easier

11
Alternate visualizations...

Dunne et al., 2012

Gove et al., 2011

Blue et al., 2008

Henry & Fekete,
2006

Freire et al....
1. Tools for network analysis that are easy
to learn, powerful, and insightful

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19
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NodeXL Graph Gallery

34
NodeXL as a Teaching Tool
I. Getting Started with Analyzing Social Media Networks
1. Introduction to Social Media and Soci...
NodeXL as a Research Tool

36
NodeXL Results
• Easy to learn, yet powerful and insightful
• Widely used by both students and researchers
• Free and open...
2. Visualize complex relationships with
limited screen space

38
Lostpedia articles

Observations
1: There are repeating patterns in
networks (motifs)
2: Motifs often dominate the
visuali...
Graph Summarization…

Navlakha et al., 2008 40
Motif Simplification
Fan Motif

2-Connector Motif

41
Lostpedia articles

42
Lostpedia articles

43
Glyph Design: Fan

44
Glyph Design: Connector

45
Cliques too!

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Interactivity

Fan motif: 133 leaf vertices
with head vertex “Theory”

47
Interactivity in NodeXL

48
Senate Co-Voting: 65%
Agreement

49
Senate Co-Voting: 70%
Agreement

50
Senate Co-Voting: 80%
Agreement

51
Voson Web Crawl
Voson Web Crawl
Voson Web Crawl
Motif Simplification Results
• Controlled experiment with 36 users showed that
motif simplification improves user task per...
3. Explore groups in the network, including
their size, membership, and relationships

56
57
Previous Meta-Layouts
• Poorly show ties (Rodrigues et al., 2011)
• Long ties
• Group arrangement
• Aggregate relationship...
Group-in-a-Box Meta-Layouts
• Squarified Treemap

• Croissant-Donut

• Force-Directed
59
60
Risk Movements
Plain Layout
with Clusters

61
Risk Movements
GIB Treemap

62
Risk Movements
GIB Croissant

63
Risk Movements
GIB Force-Directed

64
Meta-Layout Results
• Three Group-in-a-Box layout algorithms for
dissecting networks
• Improved group and overview visuali...
Available Now in NodeXL!
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Motif Simplification
Group-in-a-Box Layouts
Data import spigots
Excel ...
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Boston DataSwap 2013 -- Network Visualization in NodeXL

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Boston DataSwap 2013 -- Network Visualization in NodeXL

  1. 1. Network Visualization in NodeXL Cody Dunne IBM Research – Cambridge, MA cdunne@us.ibm.com Boston Data Swap Skill-A-Thon Oct. 17, 2013 1
  2. 2. The Data Problem 2
  3. 3. 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 3
  4. 4. 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 4
  5. 5. Anscombe’s Quartet - Scatterplots 5
  6. 6. No catalogue of techniques can convey a willingness to look for what can be seen, whether or not anticipated. Yet this is at the heart of exploratory data analysis. ... the picture-examining eye is the best finder we have of the wholly unanticipated. – Tukey, 1980 6
  7. 7. Node-Link Network Visualization Node 1 Node 2 Alice Bob Alice Cathy Cathy Alice 7
  8. 8. Tweets of the #Win09 Workshop # User 1 User 2 # User 1 User 2 1 20andlife barrywellman 15 danevans87 informor 2 20andlife BrianDavidson 16 danevans87 NetSciWestPoint 3 barrywellman elizabethmdaly 17 danielequercia BrianDavidson 4 barrywellman informor 18 danielequercia drewconway 5 BrianDavidson hcraygliangjie 19 danielequercia ipeirotis 6 BrianDavidson informor 20 danielequercia johnflurry 7 BrianDavidson NetSciWestPoint 21 danielequercia loyan 8 byaber barrywellman 22 danielequercia loyan 9 byaber danielequercia 23 danielequercia mcscharf 10 byaber mcscharf 24 danielequercia NetSciWestPoint 11 chrisnordyke RebeccaBadger 12 danevans87 barrywellman 106 sechrest Japportreport 13 danevans87 BrianDavidson 107 sechrest loyan 14 danevans87 drewconway 108 sechrest RebeccaBadger … … … 8
  9. 9. Tweets of the #Win09 Workshop 9
  10. 10. Who Uses Network Analysis Sociology Scientometrics Biology Urban Planning Politics Archaeology WWW
  11. 11. Network visualization is highly useful, but hard! There are many ways to make it easier 11
  12. 12. Alternate visualizations... Dunne et al., 2012 Gove et al., 2011 Blue et al., 2008 Henry & Fekete, 2006 Freire et al., 2010 Wattenberg, 2006 12
  13. 13. 1. Tools for network analysis that are easy to learn, powerful, and insightful 13
  14. 14. 14
  15. 15. 15
  16. 16. 16
  17. 17. 17
  18. 18. 18
  19. 19. 19
  20. 20. 20
  21. 21. 21
  22. 22. 22
  23. 23. 23
  24. 24. 24
  25. 25. 25
  26. 26. 26
  27. 27. 27
  28. 28. 28
  29. 29. 29
  30. 30. 30
  31. 31. 31
  32. 32. 32
  33. 33. 33
  34. 34. NodeXL Graph Gallery 34
  35. 35. 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 35
  36. 36. NodeXL as a Research Tool 36
  37. 37. NodeXL Results • Easy to learn, yet powerful and insightful • Widely used by both students and researchers • Free and open source sofware • World-wide team of collaborators Malik S, Smith A, Papadatos P, Li J, Dunne C, and Shneiderman B (2013), “TopicFlow: Visualizing topic alignment of Twitter data over time. In ASONAM '13. 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. 37 DOI:0.1145/1556460.1556497
  38. 38. 2. Visualize complex relationships with limited screen space 38
  39. 39. Lostpedia articles Observations 1: There are repeating patterns in networks (motifs) 2: Motifs often dominate the visualization 3: Motifs members can be functionally equivalent 39
  40. 40. Graph Summarization… Navlakha et al., 2008 40
  41. 41. Motif Simplification Fan Motif 2-Connector Motif 41
  42. 42. Lostpedia articles 42
  43. 43. Lostpedia articles 43
  44. 44. Glyph Design: Fan 44
  45. 45. Glyph Design: Connector 45
  46. 46. Cliques too! 46
  47. 47. Interactivity Fan motif: 133 leaf vertices with head vertex “Theory” 47
  48. 48. Interactivity in NodeXL 48
  49. 49. Senate Co-Voting: 65% Agreement 49
  50. 50. Senate Co-Voting: 70% Agreement 50
  51. 51. Senate Co-Voting: 80% Agreement 51
  52. 52. Voson Web Crawl
  53. 53. Voson Web Crawl
  54. 54. Voson Web Crawl
  55. 55. Motif Simplification Results • Controlled experiment with 36 users showed that motif simplification improves user task performance • Reducing complexity • Understanding larger or hidden relationships • Algorithms for detecting fans, connectors, and cliques • Publicly available implementation 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. pp. 3247-3256. DOI:10.1145/2470654.2466444 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- 55
  56. 56. 3. Explore groups in the network, including their size, membership, and relationships 56
  57. 57. 57
  58. 58. Previous Meta-Layouts • Poorly show ties (Rodrigues et al., 2011) • Long ties • Group arrangement • Aggregate relationships OR • Poorly show nodes & groups (Noack, 2003) • Require much more space • Harder to see groups 58
  59. 59. Group-in-a-Box Meta-Layouts • Squarified Treemap • Croissant-Donut • Force-Directed 59
  60. 60. 60
  61. 61. Risk Movements Plain Layout with Clusters 61
  62. 62. Risk Movements GIB Treemap 62
  63. 63. Risk Movements GIB Croissant 63
  64. 64. Risk Movements GIB Force-Directed 64
  65. 65. Meta-Layout Results • Three Group-in-a-Box layout algorithms for dissecting networks • Improved group and overview visualization • Empirical evaluation on 309 Twitter networks using readability metrics • Publicly available implementation 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:10.1007/978-3-64236763-2_2 Chaturvedi S, Ashktorab Z, Dunne C, Zacharia R, and Shneiderman B (2013), “Croissant-Donut and ForceDirected Group-in-a-Box layouts for clustered network visualization", In preparation. 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. 65
  66. 66. Available Now in NodeXL! • • • • • • • • • • • • • Motif Simplification Group-in-a-Box Layouts Data import spigots Excel functions & macros Network statistics Layout algorithms Filtering Clustering Attribute mapping Automate analyses Email reporting Graph Gallery C# libraries nodexl.codeplex.com Cody Dunne IBM Research – Cambridge, MA cdunne@us.ibm.com

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