First Steps to NetViz Nirvana: Evaluating Social Network Analysis with NodeXL<br />1<br />
Motivation & Goals for Study	<br />NodeXL evaluation<br />NetViz Nirvana & Readability Metrics<br />Research Methods<br />...
SNA Tools are not just for scientists anymore<br />Long-term Goal: Accessible Tools and Educational Strategies<br />How ca...
Focus for this talk<br /><ul><li> 	Evaluation of NodeXL
For teaching SNA concepts
For diverse user set
NetViz Nirvana principles & </li></ul>	Readability Metrics (RMs) <br />4<br />
Focus for this talk<br /><ul><li>Evaluation of NodeXL
For teaching SNA concepts
For diverse user set
NetViz Nirvana principles & </li></ul>	Readability Metrics (RMs) <br />5<br />
6<br />Network Overview, Discovery and Exploration for Excel<br />
7<br />Network Overview, Discovery and Exploration for Excel<br /><ul><li>    Import network data </li></ul>     from exis...
8<br />Network Overview, Discovery and Exploration for Excel<br /><ul><li>    Library of basic network metrics
    Select as Needed</li></li></ul><li>9<br />Network Overview, Discovery and Exploration for Excel<br /><ul><li>    Multi...
Focus for this talk<br /><ul><li> 	Evaluation of NodeXL
For teaching SNA concepts
For diverse user set
NetViz Nirvana principles & </li></ul>	Readability Metrics (RMs) <br />10<br />
NetViz Nirvana<br /><ul><li>Every node is visible
Every node’s degree is countable
Every edge can be followed from source to destination
Clusters and outliers are identifiable</li></ul>11<br />
Readability Metrics<br /><ul><li>How understandable is the network drawing?
Continuous scale [0,1]
Also called aesthetic metrics
Global metrics are not sufficient to guide users
Node and edge readability metrics</li></ul>12<br />
13<br />Node Occlusion RM<br /><ul><li>Proportional to the lost node area when ‘flattening’ all overlapping nodes
1: No area is lost
0: All nodes overlap completely (N-1 node areas lost)</li></ul>C<br />B<br />A<br />D<br />
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IEEE SocialCom 2009: NetViz Nirvana (NodeXL Learnability)

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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.

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IEEE SocialCom 2009: NetViz Nirvana (NodeXL Learnability)

  1. 1. First Steps to NetViz Nirvana: Evaluating Social Network Analysis with NodeXL<br />1<br />
  2. 2. Motivation & Goals for Study <br />NodeXL evaluation<br />NetViz Nirvana & Readability Metrics<br />Research Methods<br />Samples of Student Work<br />Lessons Learned<br />Educators<br />Designers<br />Researchers<br />2<br />
  3. 3. SNA Tools are not just for scientists anymore<br />Long-term Goal: Accessible Tools and Educational Strategies<br />How can we support practitioners to cultivate <br />sustainable online communities?<br />Create Your Own<br />Social Network Site<br />Images courtesy of: Luc Legay’s twitter & facebook network visualizations (http://www.flickr.com/photos/luc/1824234195/in/set-72157605210232207/)<br />and http://prblog.typepad.com,<br />
  4. 4. Focus for this talk<br /><ul><li> Evaluation of NodeXL
  5. 5. For teaching SNA concepts
  6. 6. For diverse user set
  7. 7. NetViz Nirvana principles & </li></ul> Readability Metrics (RMs) <br />4<br />
  8. 8. Focus for this talk<br /><ul><li>Evaluation of NodeXL
  9. 9. For teaching SNA concepts
  10. 10. For diverse user set
  11. 11. NetViz Nirvana principles & </li></ul> Readability Metrics (RMs) <br />5<br />
  12. 12. 6<br />Network Overview, Discovery and Exploration for Excel<br />
  13. 13. 7<br />Network Overview, Discovery and Exploration for Excel<br /><ul><li> Import network data </li></ul> from existing spreadsheets<br /><ul><li>…Or, from several common</li></ul>social network data sources<br />
  14. 14. 8<br />Network Overview, Discovery and Exploration for Excel<br /><ul><li> Library of basic network metrics
  15. 15. Select as Needed</li></li></ul><li>9<br />Network Overview, Discovery and Exploration for Excel<br /><ul><li> Multiple ways to map data </li></ul> to display properties<br />
  16. 16. Focus for this talk<br /><ul><li> Evaluation of NodeXL
  17. 17. For teaching SNA concepts
  18. 18. For diverse user set
  19. 19. NetViz Nirvana principles & </li></ul> Readability Metrics (RMs) <br />10<br />
  20. 20. NetViz Nirvana<br /><ul><li>Every node is visible
  21. 21. Every node’s degree is countable
  22. 22. Every edge can be followed from source to destination
  23. 23. Clusters and outliers are identifiable</li></ul>11<br />
  24. 24. Readability Metrics<br /><ul><li>How understandable is the network drawing?
  25. 25. Continuous scale [0,1]
  26. 26. Also called aesthetic metrics
  27. 27. Global metrics are not sufficient to guide users
  28. 28. Node and edge readability metrics</li></ul>12<br />
  29. 29. 13<br />Node Occlusion RM<br /><ul><li>Proportional to the lost node area when ‘flattening’ all overlapping nodes
  30. 30. 1: No area is lost
  31. 31. 0: All nodes overlap completely (N-1 node areas lost)</li></ul>C<br />B<br />A<br />D<br />
  32. 32. 14<br />A<br />Edge Crossing RM<br /><ul><li>Number of crossings scaled by approximate upper bound</li></ul>C<br />B<br />D<br />
  33. 33. 15<br />Edge Tunnel RM<br /><ul><li>Number of tunnels scaled by approximate upper bound
  34. 34. Local Edge Tunnels
  35. 35. Triggered Edge Tunnels</li></ul>C<br />B<br />A<br />D<br />
  36. 36. 16<br />Label Height RMs<br /><ul><li>Text height should have a visual angle within 20-22 minutes of arc</li></li></ul><li>17<br />Label Distinctiveness<br /><ul><li>Every label should be uniquely identifiable
  37. 37. Prefix trees find all identical labels at any truncation length</li></li></ul><li>Qualitative Theoretical Foundation<br />Multi-Dimensional In-depth Long-term Case Studies Approach (MILCs)<br />Ideal for studying how users explore complex data sets<br />Two-Pronged User Survey<br />Core Set of Data Collection Methods<br />Length & Focus tailored to background of each group<br />18<br />
  38. 38. 19<br />
  39. 39. 20<br />
  40. 40. Salient issues: Learning & Teaching SNA<br />Students enjoy mapping display properties for nodes & edges that reflect the actors & relations they represent<br />NodeXL effectively supports this integration of data & visualization<br />Students strove to achieve NetViz Nirvana<br />21<br />
  41. 41. 22<br />Use of NodeXL to<br /><ul><li> Identify Boundary Spanners across sub-groups of Ravelry community
  42. 42. Gain insight on factors leading to high # of completed projects</li></li></ul><li>23<br />Node Color == Betweenness Centrality<br />Node Size == Eigenvector Centrality<br />Use of NodeXL to<br /><ul><li> Confirm hypotheses about key characteristics for listserv admin
  43. 43. Model a potential management problem with ease</li></li></ul><li>24<br />Lessons Learned for Educators<br /><ul><li> Promote awareness of layout considerations (NetViz Nirvana)
  44. 44. Scaffold learning with interaction history & “undo” actions
  45. 45. Pacing issues
  46. 46. Higher level of Excel experience desirable</li></li></ul><li>25<br />Lessons Learned for Researchers<br /><ul><li>MILCs more representative of exploratory analysis than traditional usability tests
  47. 47. MILCs also more representative of the learning process
  48. 48. MILCs require more intensive data collection & analysis</li></li></ul><li>26<br />Lessons Learned for Designers<br /><ul><li> Multiple coordinated views (data, visualization, statistics)
  49. 49. Encode visual elements with individual & community attributes
  50. 50. Add RM interactions (based on NetViz Nirvana)
  51. 51. Extensible data manipulation
  52. 52. Track interaction history & “undo” actions
  53. 53. Improved edge & node aggregation</li></li></ul><li>Research Methods<br />User pool represented diversity & depth<br />SNA Education<br />IS user results showcased NodeXL’s power as a learning & teaching tool for SNA<br />NodeXL Usability and Design<br />CS user feedback enabled rapid implementation of requested features & fixes during the study & beyond <br />27<br />
  54. 54. Questions?<br />http://casci.umd.edu/NodeXL_Teaching<br />http://www.codeplex.com/NodeXL<br />http://www.cs.umd.edu/hcil/research/visualization.shtml<br />Thank you!<br />28<br />Cody Dunne cdunne@cs.umd.edu<br />Elizabeth Bonsignore ebonsign@umd.edu<br />
  55. 55. 29<br />backup slides follow <br />(extra student graph for MSR talk)<br />
  56. 56. 30<br />KEY<br />Sub-Groups<br /> Community Leaders<br /> Hosts<br />Use of NodeXL to<br /><ul><li> Identify Boundary Spanners in the </li></ul>Subaru Owners’ sub-group<br /><ul><li> Show levels of participation in different forums (edge width)</li></ul>Carspace community logo courtesy of Edmund’s CarSpace: http://www.carspace.com/<br />
  57. 57. First Steps to NetViz Nirvana: Evaluating Social Network Analysis with NodeXL<br />Elizabeth Bonsignore, Cody Dunne<br />Dana Rotman, Marc Smith, Tony Capone, Derek L. Hansen, Ben Shneiderman<br />31<br />
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