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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  • http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
  • http://www.flickr.com/photos/lizjones/1571656758/sizes/o/
  • http://www.flickr.com/photos/kjander/3123883124/sizes/o/
  • http://www.flickr.com/photos/badgopher/3264760070/
  • http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
  • http://www.flickr.com/photos/amycgx/3119640267/
  • A tutorial on analyzing social media networks is available from: casci.umd.edu/NodeXL_TeachingDifferent positions within a network can be measured using network metrics.
  • http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html
  • The network of connections among people who tweeted “#My2K” over the 1-day, 21-hour, 39-minute period from Sunday, 06 January 2013 at 03:30 UTC to Tuesday, 08 January 2013 at 01:09 UTC.
  • The graph represents a network of 268 Twitter users whose recent tweets contained "#cmgrchat OR #smchat. The network was obtained on Friday, 18 January 2013 at 15:44 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 3-day, 21-hour, 15-minute period from Monday, 14 January 2013 at 18:23 UTC to Friday, 18 January 2013 at 15:38 UTC.
  • The graph represents a network of 1,227 Twitter users whose recent tweets contained "lumia. The network was obtained on Saturday, 12 January 2013 at 19:52 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 5-hour, 1-minute period from Saturday, 12 January 2013 at 14:36 UTC to Saturday, 12 January 2013 at 19:37 UTC.
  • The graph represents a network of 1,260 Twitter users whose recent tweets contained "flotus". The network was obtained on Friday, 18 January 2013 at 18:26 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 3-hour, 3-minute period from Friday, 18 January 2013 at 15:16 UTC to Friday, 18 January 2013 at 18:20 UTC.
  • The graph represents a network of 399 Twitter users whose recent tweets contained "http://www.nytimes.com/2013/01/11/opinion/krugman-coins-against-crazies.html. The network was obtained on Friday, 11 January 2013 at 14:27 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 12-hour, 32-minute period from Friday, 11 January 2013 at 01:52 UTC to Friday, 11 January 2013 at 14:24 UTC.
  • The graph represents a network of 388 Twitter users whose recent tweets contained "delllistens OR dellcares”. The network was obtained on Tuesday, 19 February 2013 at 17:44 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 6-day, 21-hour, 58-minute period from Tuesday, 12 February 2013 at 19:34 UTC to Tuesday, 19 February 2013 at 17:33 UTC.
  • https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=17822
  • https://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=16540strataconf Twitter NodeXL SNA Map and Report for 2014-02-11 12-53-27The graph represents a network of 1,685 Twitter users whose recent tweets contained "strataconf", tweeted over the 8-day, 0-hour, 44-minute period from Monday, 03 February 2014 at 19:55 UTC to Tuesday, 11 February 2014 at 20:39 UTC.Top Hashtags in Tweet in Entire Graph:#Strataconf, #bigdata, #hds, #BigDataSV, #hadoop, #ddbd
  • https://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=16541datavis Twitter NodeXL SNA Map and Report for Tuesday, 11 February 2014 at 18:55 UTCThe graph represents a network of Twitter users whose tweets in the requested date range contained "dataviz OR datavis“ over the 41-day, 4-hour, 5-minute period from Wednesday, 01 January 2014 at 00:01 UTC to Tuesday, 11 February 2014 at 04:06 UTCTop Hashtags in Tweet in Entire Graph:#dataviz, #bigdata,#analytics,#map,#Europe, #Datavis,#Audit,#Logs
  • http://www.pewresearch.org/fact-tank/2014/02/20/the-six-types-of-twitter-conversations/
  • http://www.katypearce.net/protestbaku-analysis-the-day-after/
  • http://portal.sliderocket.com/ATWBE/Using-SNA-to-find-and-manage-RICsC. Scott Dempwolf, PhDResearch Assistant Professor & DirectorUMD - Morgan State Center for Economic Developmenthttp://www.terpconnect.umd.edu/~dempy/Insights: many clusters are based around a county and local enterprises. E.g., the middle-left cluster is Pittsburgh metro area, with large orange Westinghouse Electric. The Philadelphia cluster in the top-right is highly connected to the bottom left, which are adjacent counties. An exception to location grouping is the top-left pharma and medical cluster, composed of several companies, universities, HHS, and an interesting arrangement of inventors in several connected fans.https://plus.google.com/photos/116499393494903612852/albums/5659635437858992593/5659734868308985794?banner=pwa&pid=5659734868308985794&oid=116499393494903612852
  • Prof. Diane Clinehttp://www.academia.edu/2153390/The_Social_network_of_Alexander_the_Great_Social_Network_Analysis_in_Ancient_HistoryIt’s about who you know, and who those people know, and how everyone knows each other.Data visualization tool – to see data differently.
  • 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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