Google nyc-6-3-2011

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Talk in Google- NYC, 6/3/2011 covering Information Visualization
Analyzing Social Media Networks with NodeXL

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  • "The IN Cell Analyzer automated microscope was used to identify proteins influencing the division of human cells. After the images were analyzed, quantitative results were transferred to Spotfire DecisionSite. This screen revealed the previously unknown involvement of the retinol binding protein RBP1 in cell cycle control.(Stubbs S, & Thomas N. 2006 Methods in Enzymology; 414:1-21.) Retinol a form of Vitamin A plays a crucial role in vision and during embryonic development"  
  • Contrast and Creatinine dataset In some diagnostic radiology procedures, patients are injected contrast material. However, some patients develop adverse side effects to the contrast material. One serious side effect is renal failure, which is detected by high creatinine levels in a patient's blood. This adverse effect usually occur within two weeks after the radiology contrast. WHC is interested in finding the proportion of patients who exhibit this condition in historical records. Screenshots 1-aligned-ranked.png: We align by the 1st occurrence of radiology contrast and rank by the number of creatinine high (CREAT-H) events to bring the most severe patients to the top. We realize two things: (1) some patients have more than 1 "Radiology Contrast" events, and (2), some patients have consistently high creatinine readings (chronic kidney failure). 2-aligned(all)-distribution-selected.png We align by all occurrences of raiology contrast, and then show the temporal summary of CREAT-H events. The patients are presented in 4 exclusive sets in the summary: those who have CREAT-H only before alignment, only after alignment, both before and after, and neither. We then select from the "only after" summary the patients who have at least one CREAT-H event within 2 weeks of any "Radiology Contrast" event. There are 421 patients.
  • Live Demonstration
  • Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#WIN09” used by attendees of a conference on Network Science at NYU in September 2009. Each user’s node is sized proportional to the number of tweets they have ever made to that date.
  • “ HCI” twitter stream shows ‘human capital index’ community Jun Rekimoto with 160,000 followers.
  • Figure 13.20. NodeXL cluster visualization showing three Flickr tag clusters, each representing a different context for “mouse”. Figure 13.21. NodeXL display of Isolated clusters for three different contexts for the “mouse” tag in Flickr: mouse animal, computer mouse, and Mickey Mouse Disney character.
  • Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#WIN09” used by attendees of a conference on Network Science at NYU in September 2009. Each user’s node is sized proportional to the number of tweets they have ever made to that date.
  • Google nyc-6-3-2011

    1. 1. Analyzing Social Media Networks with NodeXL Ben Shneiderman ben@cs.umd.edu @benbendcFounding Director (1983-2000), Human-Computer Interaction Lab Professor, Department of Computer Science Member, Institute for Advanced Computer Studies University of Maryland College Park, MD 20742
    2. 2. Interdisciplinary research community - Computer Science & Info Studies - Psych, Socio, Poli Sci & MITH (www.cs.umd.edu/hcil)
    3. 3. Design Issues• Input devices & strategies • Keyboards, pointing devices, voice • Direct manipulation • Menus, forms, commands• Output devices & formats • Screens, windows, color, sound • Text, tables, graphics • Instructions, messages, help• Collaboration & Social Media www.awl.com/DTUI Fifth Edition: 2010• Help, tutorials, training• Search • Visualization
    4. 4. Information Visualization• Visual bandwidth is enormous • Human perceptual skills are remarkable • Trend, cluster, gap, outlier... • Color, size, shape, proximity...• Three challenges • Meaningful visual displays of massive data • Interaction: widgets & window coordination • Process models for discovery:
    5. 5. Leader in Info Visualization
    6. 6. Business takes action• General Dynamics buys MayaViz• Agilent buys GeneSpring• Google buys Gapminder• Oracle buys Hyperion• Microsoft buys Proclarity• InfoBuilders buys Advizor Solutions• SAP buys (Business Objects buys Xcelsius & Inxight & Crystal Reports )• IBM buys (Cognos buys Celequest) & ILOG• TIBCO buys Spotfire
    7. 7. Spotfire: Retinol’s role in embryos & vision
    8. 8. http://registration.spotfire.com/eval/default_edu.asp
    9. 9. 10M - 100M pixels Large displays  for single or multiple users
    10. 10. 100M-pixels & more
    11. 11. 1M-pixels & less Small mobile devices
    12. 12. Information Visualization: Mantra• Overview, zoom & filter, details-on-demand• Overview, zoom & filter, details-on-demand• Overview, zoom & filter, details-on-demand• Overview, zoom & filter, details-on-demand• Overview, zoom & filter, details-on-demand• Overview, zoom & filter, details-on-demand• Overview, zoom & filter, details-on-demand• Overview, zoom & filter, details-on-demand• Overview, zoom & filter, details-on-demand• Overview, zoom & filter, details-on-demand
    13. 13. Information Visualization: Data Types • 1-D LinearSciViz . Document Lens, SeeSoft, Info Mural • 2-D Map GIS, ArcView, PageMaker, Medical imagery • 3-D World CAD, Medical, Molecules, Architecture • Multi-Var Spotfire, Tableau, GGobi, TableLens, ParCoords, • Temporal LifeLines, TimeSearcher, Palantir, DataMontageInfoViz • Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap • Network Pajek, JUNG, UCINet, SocialAction, NodeXL infosthetics.com flowingdata.com infovis.org eagereyes.org www.infovis.net/index.php?lang=2
    14. 14. Anscombe’s Quartet 1 2 3 4x y x y x y x y10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.7613.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.8411.0 8.33 11.0 9.26 11.0 7.81 8.0 8.4714.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.5012.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
    15. 15. Anscombe’s Quartet 1 2 3 4x y x y x y x y Property Value10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 Mean of x 9.0 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 Variance of x 11.013.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 Mean of y 7.5 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 Variance of y 4.1211.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 Correlation 0.81614.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 Linear regression y = 3 + 0.5x 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.5012.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
    16. 16. Anscombe’s Quartet
    17. 17. Temporal Data: TimeSearcher 1.3• Time series • Stocks • Weather • Genes• User-specified patterns• Rapid search
    18. 18. Temporal Data: TimeSearcher 2.0• Long Time series (>10,000 time points)• Multiple variables• Controlled precision in match (Linear, offset, noise, amplitude)
    19. 19. LifeLines: Patient Histories www.cs.umd.edu/hcil/lifelines
    20. 20. LifeLines2: Contrast+Creatine
    21. 21. LifeLines2: Align-Rank-Filter & Summarize
    22. 22. Treemap: Gene Ontology+ Space filling+ Space limited+ Color coding+ Size coding- Requires learning (Shneiderman, ACM Trans. on Graphics, 1992 & 2003) www.cs.umd.edu/hcil/treemap/
    23. 23. Treemap: Smartmoney MarketMap www.smartmoney.com/marketmap
    24. 24. Market falls steeply Feb 27, 2007, with one exception
    25. 25. Market mixed, February 8, 2008Energy & Technology up, Financial & Health Care down
    26. 26. Market rises, September 1, 2010, Gold contrarians
    27. 27. Market rises, March 21, 2011, Sprint declines
    28. 28. Treemap: Newsmap (Marcos Weskamp) newsmap.jp
    29. 29. Treemap: Supply Chain www.hivegroup.com
    30. 30. Treemap: Spotfire Bond Portfolio Analysis www.spotfire.com
    31. 31. Treemap: NY Times – Car&Truck Sales www.cs.umd.edu/hcil/treemap/
    32. 32. Treemap (Voronoi): NY Times - Inflationwww.nytimes.com/interactive/2008/05/03/business/20080403_SPENDING_GRAPHIC.html
    33. 33. State-of-the-art network visualization
    34. 34. Discovery Process: Systematic Yet Flexible Preparation • Own the problem & define the schedule • Data cleaning & conditioning • Handle missing & uncertain data • Extract subsets & link to related information
    35. 35. SocialAction• Integrates statistics & visualization• 4 case studies, 4-8 weeks (journalist, bibliometrician, terrorist analyst, organizational analyst)• Identified desired features, gave strong positive feedback about benefits of integration www.cs.umd.edu/hcil/socialaction Perer & Shneiderman, CHI2008, IEEE CG&A 2009
    36. 36. Footprints of Human Activity• Footprints in sand as Caesarea
    37. 37. NodeXL: Network Overview for Discovery & Exploration in Excelwww.codeplex.com/nodexl
    38. 38. NodeXL:Network Overview for Discovery & Exploration in Excel www.codeplex.com/nodexl
    39. 39. NodeXL: Import Dialogs www.codeplex.com/nodexl
    40. 40. Tweets at #WIN09 Conference: 2 groups
    41. 41. WWW2010 Twitter Community
    42. 42. WWW2011 Twitter Community: Grouped
    43. 43. Tweets for #HCI: 2 groups
    44. 44. CHI2010 Twitter Community www.codeplex.com/nodexl/
    45. 45. Oil Spill Twitter Community www.codeplex.com/nodexl/
    46. 46. ‘GOP’ tweets, clustered (red-Republicans)
    47. 47. Flickr clusters for “mouse” Computer          Mickey Animal
    48. 48. Flickr networks
    49. 49. Co-author network for HCIL tech reports Vertices sized by number of papers.   Edges sized number of co­authored  reports.  Colored by clustering.
    50. 50. Co-author network for HCIL tech reports Vertices sized by    number of papers,    edges sized number    of co­authored reports Colored by date    of first paper.   Includes only those    with at least    5 co­authored papers.
    51. 51. Nation of Neighbors Discussion Groups
    52. 52. Analyzing Social Media Networks with NodeXLI. 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 AnalysisII. NodeXL Tutorial: Learning by Doing      4. Layout, Visual Design & Labeling     5. Calculating & Visualizing Network Metrics      6. Preparing Data & Filtering     7. Clustering &GroupingIII Social Media Network Analysis Case Studies      8. Email     9. Threaded Networks   10. Twitter   11. Facebook     12. WWW   13. Flickr   14. YouTube    15. Wiki Networks  www.elsevier.com/wps/find/bookdescription.cws_home/723354/description
    53. 53. Social Media Research FoundationResearchers who want to    ­ create open tools   ­ generate & host open data   ­ support open scholarship Map, measure & understand     social media   Support tool projects to   collection, analyze & visualize   social media data.   smrfoundation.org
    54. 54. UN Millennium Development GoalsTo be achieved by 2015 • Eradicate extreme poverty and hunger • Achieve universal primary education • Promote gender equality and empower women • Reduce child mortality • Improve maternal health • Combat HIV/AIDS, malaria and other diseases • Ensure environmental sustainability • Develop a global partnership for development
    55. 55. Just happened: 28th Annual Symposium May 25-26, 2011Next Event: Summer Social Webshop August 23-26, 2011 (Sponsored by NSF & Google) www.cs.umd.edu/hcil/webshop2011
    56. 56. For More Information• Visit the HCIL website for 400 papers & info on videos www.cs.umd.edu/hcil• Conferences & resources: www.infovis.org• See Chapter 14 on Info Visualization Shneiderman, B. and Plaisant, C., Designing the User Interface: Strategies for Effective Human-Computer Interaction: Fifth Edition (March 2009) www.awl.com/DTUI• Edited Collections: Card, S., Mackinlay, J., and Shneiderman, B. (1999) Readings in Information Visualization: Using Vision to Think Bederson, B. and Shneiderman, B. (2003) The Craft of Information Visualization: Readings and Reflections
    57. 57. For More Information• Treemaps • HiveGroup: www.hivegroup.com • Smartmoney: www.smartmoney.com/marketmap • HCIL Treemap 4.0: www.cs.umd.edu/hcil/treemap• Spotfire: www.spotfire.com• TimeSearcher: www.cs.umd.edu/hcil/timesearcher• NodeXL: nodexl.codeplex.com• Hierarchical Clustering Explorer: www.cs.umd.edu/hcil/hce• LifeLines2: www.cs.umd.edu/hcil/lifelines2• Similan: www.cs.umd.edu/hcil/similan

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