- 1.Network Visualization in NodeXL Cody Dunne IBM Research
Cambridge, MA [email protected] Boston Data Swap Skill-A-Thon Oct.
17, 20131
2. The Data Problem2 3. Anscombes Quartet I xII yxIII yxIV
yxy10.008.0410.009.1410.007.468.006.588.006.958.008.148.006.778.005.7613.007.5813.008.7413.0012.748.007.719.008.819.008.779.007.118.008.8411.008.3311.009.2611.007.818.008.4714.009.9614.008.1014.008.848.007.046.007.246.006.136.006.088.005.254.004.264.003.104.005.3919.0012.5012.0010.8412.009.1312.008.158.005.567.004.827.007.267.006.428.007.915.005.685.004.745.005.738.006.89
3 4. Anscombes Quartet - Statistics PropertyValueEqualityMean of x
in each case9ExactVariance of x in each case11ExactMean of y in
each case7.50To 2 decimal placesVariance of y in each case4.122 or
4.127To 3 decimal placesCorrelation between x and 0.816 y in each
case Linear regression line in each caseTo 3 decimal placesTo 2 and
3 decimal y = 3.00 + 0.500x places, respectively 4 5. Anscombes
Quartet - Scatterplots5 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, 19806 7. Node-Link Network
VisualizationNode 1Node 2AliceBobAliceCathyCathyAlice7 8. Tweets of
the #Win09 Workshop #User 1User 2#User 1User 21
20andlifebarrywellman15 danevans87informor2
20andlifeBrianDavidson16 danevans87NetSciWestPoint3
barrywellmanelizabethmdaly17 danielequerciaBrianDavidson4
barrywellmaninformor18 danielequerciadrewconway5
BrianDavidsonhcraygliangjie19 danielequerciaipeirotis6
BrianDavidsoninformor20 danielequerciajohnflurry7
BrianDavidsonNetSciWestPoint21 danielequercialoyan8
byaberbarrywellman22 danielequercialoyan9 byaberdanielequercia23
danielequerciamcscharf10 byabermcscharf24
danielequerciaNetSciWestPoint11 chrisnordykeRebeccaBadger12
danevans87barrywellman106 sechrestJapportreport13
danevans87BrianDavidson107 sechrestloyan14 danevans87drewconway108
sechrestRebeccaBadger 8 9. Tweets of the #Win09 Workshop9 10. Who
Uses Network AnalysisSociologyScientometricsBiologyUrban
PlanningPoliticsArchaeologyWWW 11. Network visualization is highly
useful, but hard!There are many ways to make it easier11 12.
Alternate visualizations...Dunne et al., 2012Gove et al., 2011Blue
et al., 2008Henry & Fekete, 2006Freire et al., 2010Wattenberg,
2006 12 13. 1. Tools for network analysis that are easy to learn,
powerful, and insightful13 14. 14 15. 15 16. 16 17. 17 18. 18 19.
19 20. 20 21. 21 22. 22 23. 23 24. 24 25. 25 26. 26 27. 27 28. 28
29. 29 30. 30 31. 31 32. 32 33. 33 34. NodeXL Graph Gallery34 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
Networkshttp://www.elsevier.com/wps/find/bookdescription.cws_home/723354/description
35 36. NodeXL as a Research Tool36 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. 2. Visualize complex relationships with limited screen space38
39. Lostpedia articlesObservations 1: There are repeating patterns
in networks (motifs) 2: Motifs often dominate the visualization 3:
Motifs members can be functionally equivalent 39 40. Graph
SummarizationNavlakha et al., 2008 40 41. Motif Simplification Fan
Motif2-Connector Motif41 42. Lostpedia articles42 43. Lostpedia
articles43 44. Glyph Design: Fan44 45. Glyph Design: Connector45
46. Cliques too!46 47. InteractivityFan motif: 133 leaf vertices
with head vertex Theory47 48. Interactivity in NodeXL48 49. Senate
Co-Voting: 65% Agreement49 50. Senate Co-Voting: 70% Agreement50
51. Senate Co-Voting: 80% Agreement51 52. Voson Web Crawl 53. Voson
Web Crawl 54. Voson Web Crawl 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. 3.
Explore groups in the network, including their size, membership,
and relationships56 57. 57 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 groups58 59. Group-in-a-Box
Meta-Layouts Squarified Treemap Croissant-Donut Force-Directed 59
60. 60 61. Risk Movements Plain Layout with Clusters61 62. Risk
Movements GIB Treemap62 63. Risk Movements GIB Croissant63 64. Risk
Movements GIB Force-Directed64 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. 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# librariesnodexl.codeplex.comCody Dunne
IBM Research Cambridge, MA [email protected]
