NodeXL presentation for SocialCom and for Microsoft Research (social analytics) 2009. Describes the NodeXL study with information/library science students and computer science students on the social network analysis tool, NodeXL. The study was sponsored by Microsoft Research and designed by Dr. Derek Hansen, Dana Rotman, Cody Dunne, Dr. Ben Shneiderman and Elizabeth Bonsignore.

1. First Steps to NetViz Nirvana: Evaluating Social Network Analysis with NodeXL 1

2. Motivation & Goals for Study NodeXL evaluation NetViz Nirvana & Readability Metrics Research Methods Samples of Student Work Lessons Learned Educators Designers Researchers 2

3. SNA Tools are not just for scientists anymore Long-term Goal: Accessible Tools and Educational Strategies How can we support practitioners to cultivate sustainable online communities? Create Your Own Social Network Site Images courtesy of: Luc Legay’s twitter & facebook network visualizations (http://www.flickr.com/photos/luc/1824234195/in/set-72157605210232207/) and http://prblog.typepad.com,

5. For teaching SNA concepts

6. For diverse user set

7. NetViz Nirvana principles & Readability Metrics (RMs) 4

9. For teaching SNA concepts

10. For diverse user set

11. NetViz Nirvana principles & Readability Metrics (RMs) 5

12. 6 Network Overview, Discovery and Exploration for Excel

17. For teaching SNA concepts

18. For diverse user set

19. NetViz Nirvana principles & Readability Metrics (RMs) 10

21. Every node’s degree is countable

22. Every edge can be followed from source to destination

23. Clusters and outliers are identifiable11

25. Continuous scale [0,1]

26. Also called aesthetic metrics

27. Global metrics are not sufficient to guide users

28. Node and edge readability metrics12

30. 1: No area is lost

31. 0: All nodes overlap completely (N-1 node areas lost)C B A D

34. Local Edge Tunnels

35. Triggered Edge TunnelsC B A D

38. 19

39. 20

40. Salient issues: Learning & Teaching SNA Students enjoy mapping display properties for nodes & edges that reflect the actors & relations they represent NodeXL effectively supports this integration of data & visualization Students strove to achieve NetViz Nirvana 21

44. Scaffold learning with interaction history & “undo” actions

45. Pacing issues

47. MILCs also more representative of the learning process

49. Encode visual elements with individual & community attributes

50. Add RM interactions (based on NetViz Nirvana)

51. Extensible data manipulation

52. Track interaction history & “undo” actions

54. Questions? http://casci.umd.edu/NodeXL_Teaching http://www.codeplex.com/NodeXL http://www.cs.umd.edu/hcil/research/visualization.shtml Thank you! 28 Cody Dunne cdunne@cs.umd.edu Elizabeth Bonsignore ebonsign@umd.edu

55. 29 backup slides follow (extra student graph for MSR talk)

57. First Steps to NetViz Nirvana: Evaluating Social Network Analysis with NodeXL Elizabeth Bonsignore, Cody Dunne Dana Rotman, Marc Smith, Tony Capone, Derek L. Hansen, Ben Shneiderman 31