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Think Link: Network Insights with No Programming Skills

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Networks are everywhere, but the tools for end users to access, analyze, visualize and share insights into connected structures have been absent. NodeXL, the network overview discovery and exploration add-in for Excel makes network analysis as easy as making a pie chart.

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Think Link: Network Insights with No Programming Skills

  1. 1. A project from the Social Media Research Foundation: http://www.smrfoundation.org
  2. 2. About Me Introductions Marc A. Smith Chief Social Scientist Connected Action Consulting Group Marc@connectedaction.net http://www.connectedaction.net http://www.codeplex.com/nodexl http://www.twitter.com/marc_smith http://www.flickr.com/photos/marc_smith http://www.facebook.com/marc.smith.sociologist http://www.linkedin.com/in/marcasmith http://www.slideshare.net/Marc_A_Smith http://www.smrfoundation.org
  3. 3. Social Media Research Foundation http://smrfoundation.org
  4. 4. Social Media Research Foundation People Disciplines Institutions University Faculty Computer Science University of Maryland Students HCI, CSCW Oxford Internet Institute Industry Machine Learning Stanford University Independent Information Visualization Microsoft Research Researchers UI/UX Illinois Institute of Technology Developers Social Science/Sociology Connected Action Network Analysis Cornell Collective Action Morningside Analytics
  5. 5. What we are trying to do: Open Tools, Open Data, Open Scholarship • Build the “Firefox of GraphML” – open tools for collecting and visualizing social media data • Connect users to network analysis – make network charts as easy as making a pie chart • Connect researchers to social media data sources • Archive: Be the “Allen Very Large Telescope Array” for Social Media data – coordinate and aggregate the results of many user’s data collection and analysis • Create open access research papers & findings • Make “collections of connections” easy for users to manage
  6. 6. Kodak Brownie Snap- Shot Camera The first easy to use point and shoot!
  7. 7. Crowds matter
  8. 8. What we have done: Open Tools • NodeXL • Data providers (“spigots”) – ThreadMill Message Board – Exchange Enterprise Email – Voson Hyperlink – SharePoint – Facebook – Twitter – YouTube – Flickr
  9. 9. What we have done: Open Data • NodeXLGraphGallery.org – User generated collection of network graphs, datasets and annotations – Collective repository for the research community – Published collections of data from a range of social media data sources to help students and researchers connect with data of interest and relevance
  10. 10. What we have done: Open Scholarship
  11. 11. http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
  12. 12. Social Media (email, Facebook, Twitter, You Tube, and more) is all about connections from people to people. 12
  13. 13. Patterns are left behind 13
  14. 14. There are many kinds of ties…. Send, Mention, http://www.flickr.com/photos/stevendepolo/3254238329 Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in…
  15. 15. Strong ties
  16. 16. Weak ties
  17. 17. p://www.flickr.com/photos/fullaperture/81266869/ Strength of Weak ties
  18. 18. “Think Link” Nodes & Edges Is related to A BIs related to Is related to
  19. 19. “Think Link” Nodes & Edges Is related to A BIs related to Is related to
  20. 20. World Wide Web Social media must contain one or more social networks
  21. 21. Vertex1 Vertex 2 “Edge” Attribute “Vertex1” Attribute “Vertex2” Attribute @UserName1 @UserName2 value value value A network is born whenever two GUIDs are joined. Username Attributes @UserName1 Value, value Username Attributes @UserName2 Value, value A B
  22. 22. NodeXL imports “edges” from social media data sources
  23. 23. Social Networks • History: from the dawn of time! • Theory and method: 1934 -> • Jacob L. Moreno • http://en.wik ipedia.org/wi ki/Jacob_L._ Moreno Jacob Moreno’s early social network diagram of positive and negative relationships among members of a football team. Originally published in Moreno, J. L. (1934). Who shall survive? Washington, DC: Nervous and Mental Disease Publishing Company.
  24. 24. A nearly social network diagram of relationships among workers in a factory illustrates the positions different workers occupy within the workgroup. Originally published in Roethlisberger, F., and Dickson, W. (1939). Management and the worker. Cambridge, UK: Cambridge University Press.
  25. 25. Location, Location, Location
  26. 26. Position, Position, Position
  27. 27. https://www.simonsfoundation.org/quanta/20131004-the-mathematical-shape-of-things-to-come/
  28. 28. http://simonsfoundation.s3.amazonaws.com/jwplayer/BigData/Topological_Data_Analysis_Intro.mp4
  29. 29. Introduction to NodeXL Like MSPaint™ for graphs. — the Community
  30. 30. Now Available
  31. 31. Communities in Cyberspace
  32. 32. http://www.flickr.com/photos/badgopher/3264760070/
  33. 33. http://www.flickr.com/photos/druclimb/2212572259/in/photostream/
  34. 34. http://www.flickr.com/photos/hchalkley/47839243/
  35. 35. http://www.flickr.com/photos/rvwithtito/4236716778
  36. 36. http://www.flickr.com/photos/62693815@N03/6277208708/
  37. 37. Social Network Maps Reveal Key influencers in any topic. Sub-groups. Bridges.
  38. 38. Hubs
  39. 39. Bridges
  40. 40. http://www.flickr.com/photos/storm-crypt/3047698741
  41. 41. http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
  42. 42. http://www.flickr.com/photos/amycgx/3119640267/
  43. 43. Welser, Howard T., Eric Gleave, Danyel Fisher, and Marc Smith. 2007. Visualizing the Signatures of Social Roles in Online Discussion Groups. The Journal of Social Structure. 8(2). Experts and “Answer People” Discussion starters, Topic setters Discussion people, Topic setters
  44. 44. NodeXL Network Overview Discovery and Exploration add-in for Excel 2007/2010 A minimal network can illustrate the ways different locations have different values for centrality and degree
  45. 45. #occupywallstreet 15 November 2011 #teaparty 15 November 2011 http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html
  46. 46. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter social media networks
  47. 47. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter social media networks
  48. 48. #My2K Polarized
  49. 49. #CMgrChat In-group / Community
  50. 50. Lumia Brand / Public Topic
  51. 51. #FLOTUS Bazaar
  52. 52. New York Times Article Paul Krugman Broadcast: Audience + Communities
  53. 53. Dell Listens/Dellcares Support
  54. 54. SNA questions for social media: 1. What does my topic network look like? 2. What does the topic I aspire to be look like? 3. What is the difference between #1 and #2? 4. How does my map change as I intervene? What does #YourHashtag look like?
  55. 55. pawcon Twitter NodeXL SNA Map and Report for Monday, 17 March 2014 at 15:15 UTC
  56. 56. strataconf Twitter NodeXL SNA Map and Report for 2014-02-11 12-53-27 Top 10 Vertices, Ranked by Betweenness Centrality: @strataconf @peteskomoroch @acroll @oreillymedia @orthonormalruss @ayirpelle @bigdata @furrier @marketpowerplus @sassoftware
  57. 57. datavis Twitter NodeXL SNA Map and Report for Tuesday, 11 February 2014 at 18:55 UTC Top 10 Vertices, Ranked by Betweenness Centrality: @bigpupazzoverde @randal_olson @twitterdata @7of13 @yochum @edwardtufte @twittersports @grandjeanmartin @smfrogers @albertocairo
  58. 58. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter social media networks
  59. 59. http://www.pewresearch.org/fact-tank/2014/02/20/the-six-types-of-twitter-conversations/
  60. 60. [Divided]Polarize d Crowds [Unified]Tig ht Crowd [Fragmented] Brand Clusters [Clustered] Communities [In-Hub & Spoke]Broadcast Network [Out-Hub & Spoke]Support Network [Low probability] Find bridge users. Encourage shared material. [Low probability] Get message out to disconnected communities. [Possible transition] Draw in new participants. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Remove bridges, highlight divisions. [Low probability] Get message out to disconnected communities. [High probability] Draw in new participants. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [High probability] Increase retention, build connections. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Undesirable transition] Increase population, reduce connections. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Low probability] Get message out to disconnected communities. [Possible transition] Increase retention, build connections. [High probability] Increase reply rate, reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Possible transition] Get message out to disconnected communities. [High probability] Increase retention, build connections. [High probability] Increase publication of new content and regularly create content.
  61. 61. Request your own network map and report http://connectedaction.net
  62. 62. • Central tenet – Social structure emerges from – the aggregate of relationships (ties) – among members of a population • Phenomena of interest – Emergence of cliques and clusters – from patterns of relationships – Centrality (core), periphery (isolates), – betweenness • Methods – Surveys, interviews, observations, log file analysis, computational analysis of matrices (Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001) Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser University. pp.7- 16 Social Network Theory http://en.wikipedia.org/wiki/Social_network
  63. 63. SNA 101 • Node – “actor” on which relationships act; 1-mode versus 2-mode networks • Edge – Relationship connecting nodes; can be directional • Cohesive Sub-Group – Well-connected group; clique; cluster • Key Metrics – Centrality (group or individual measure) • Number of direct connections that individuals have with others in the group (usually look at incoming connections only) • Measure at the individual node or group level – Cohesion (group measure) • Ease with which a network can connect • Aggregate measure of shortest path between each node pair at network level reflects average distance – Density (group measure) • Robustness of the network • Number of connections that exist in the group out of 100% possible – Betweenness (individual measure) • # shortest paths between each node pair that a node is on • Measure at the individual node level • Node roles – Peripheral – below average centrality – Central connector – above average centrality – Broker – above average betweenness E D F A CB H G I C D E A B D E
  64. 64. NodeXL Free/Open Social Network Analysis add-in for Excel 2007/2010 makes graph theory as easy as a pie chart, with integrated analysis of social media sources. http://nodexl.codeplex.com
  65. 65. http://www.youtube.com/watch?v=0M3T65Iw3Ac NodeXLVideo
  66. 66. Goal: Make SNA easier • Existing Social Network Tools are challenging for many novice users • Tools like Excel are widely used • Leveraging a spreadsheet as a host for SNA lowers barriers to network data analysis and display
  67. 67. Twitter Network for “Microsoft Research” *BEFORE*
  68. 68. Twitter Network for “Microsoft Research” *AFTER*
  69. 69. Network Motif Simplification Cody Dunne, University of Maryland
  70. 70. Network Motif Simplification D-connector (glyph on the right) D-clique (glyphs for 4, 5, and 6 member cliques below) Dr. Cody Dunne Fan(glyph on the right)
  71. 71. NodeXL Graph Gallery
  72. 72. Scholars using NodeXL • Communications – Katy Pearce – Itai Himelboim • Business – Scott Dempwolf • Humanities/Classics – Diane Cline
  73. 73. C. Scott Dempwolf, PhD Research Assistant Professor & Director UMD - Morgan State Center for Economic Development
  74. 74. What is Social Network Analysis? How is it useful for the humanities? 1. New framework for analysis 2. Data visualization allows new perspectives – less linear, more comprehensive Social Network Analysis and Ancient History Diane H. Cline, Ph.D. University of Cincinnati
  75. 75. Strategies for social media engagement based on social media network analysis
  76. 76. NodeXL calculates metrics about networks and content
  77. 77. The Content summary spreadsheet displays the most frequently used URLs, hashtags, and user names within the network as a whole and within each calculated sub-group.
  78. 78. NodeXL Ribbon in Excel
  79. 79. NodeXL data import sources
  80. 80. Example NodeXL data importer for Twitter
  81. 81. NodeXL imports “edges” from social media data sources
  82. 82. NodeXL creates a list of “vertices” from imported social media edges NodeXL displays subgraph images along with network metadata
  83. 83. NodeXL Automation makes analysis simple and fast Perform collections of common operations with a single click
  84. 84. NodeXL Generates Overall Network Metrics
  85. 85. What we want to do: (Build the tools to) map the social web • Move NodeXL to the web: (Node[NOT]XL) – Node for Google Doc Spreadsheets? – WebGL Canvas? D3.JS? Sigma.JS • Connect to more data sources of interest: – RDF, MediaWikis, Gmail, NYT, Citation Networks • Solve hard network manipulation UI problems: – Modal transform, Time series, Automated layouts • Grow and maintain archives of social media network data sets for research use. • Improve network science education: – Workshops on social media network analysis – Live lectures and presentations – Videos and training materials
  86. 86. How you can help • Sponsor a feature • Sponsor workshops • Sponsor a student • Schedule training • Sponsor the foundation • Donate your money, code, computation, storage, bandwidth, data or employee’s time • Help promote the work of the Social Media Research Foundation
  87. 87. A project from the Social Media Research Foundation: http://www.smrfoundation.org

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