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Introduction to Social Network Analysis

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Two part presentation on concepts of network analysis and the tools that are available.

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Introduction to Social Network Analysis

  1. 1. Network Analysis in Two Parts (with an Introduction) Patti Anklam Columbia IKNS Unit 4 April 2016
  2. 2. Introduction: Graph Theory Put to Work
  3. 3. Columbia IKNS Residency April 2016 Origins of Network Study • Graph theory – Euler, the seven bridges of Königsberg (1736) • Sociometry – Jacob Moreno, Hudson Training School for Girls (1932) 3
  4. 4. Columbia IKNS Residency April 2016 Symposium on Social Networks: Dartmouth, 1975 http://eclectic.ss.uci.edu/~drwhite/Networks/MSSB1975.html
  5. 5. Columbia IKNS Residency April 2016 2007 Network Theory Reaches the Business World 2002 2002 2002 2003 2004 2004 5 2005 2009 2009 2002 2002
  6. 6. Columbia IKNS Residency April 2016 Organizational Networks 6 Source: MWH Global, Vic Gulas
  7. 7. Columbia IKNS Residency April 2016 Disease and Health 7
  8. 8. Columbia IKNS Residency April 2016 Networks of Companies 8 Source: Laurie Lock Lee, http://www.optimice.com.au Equipment Manufacturers Systems integrators
  9. 9. Columbia IKNS Residency April 2016 https://kumu.io/UnLtdUSA/austin-social-entrepreneurship People and Companies 9 Austin Social Entrepreneurship
  10. 10. Columbia IKNS Residency April 2016 Mapping Ideas and Topics 10 http://www.smrfoundation.org/2009/09/12/networks-in-the-news-news-dots-on-slate/
  11. 11. Columbia IKNS Residency April 2016 Showing Affiliations 11
  12. 12. Columbia IKNS Residency April 2016 The Premise: Networks Matter • Social Capital – People with stronger personal networks are more productive, happier, and better performers – Companies who know how to manage alliances are more flexible, adaptive and resilient – Our personal health and well-being is often tied to our social networks • Making Sense – Once we have the distinction “network” then we can use our knowledge of the networks we live in to make sense 12
  13. 13. Columbia IKNS Residency April 2016 The Opportunity: Leverage the Science 13 • Graph theory provided the underlying math and science to help us make sense of the network structure • The structure of a network provides insights into network patterns: • About the structure of the network • About people in the network • Once you understand the structure, you can make decisions about how to manage the network’s context – this is Net Work
  14. 14. I’ve become convinced that understanding how networks work is an essential 21st century literacy. Howard Rheingold
  15. 15. Columbia IKNS Residency April 2016 The Importance of Understanding Networks 15 Burt, Ronald S. and Don Ronchi, Teaching executives to see social capital: Results from a field experiment http://faculty.chicagobooth.edu/ronald.burt/research/files/TESSC.pdf
  16. 16. Columbia IKNS Residency April 2016 The Two Parts ―The language of networks ―Networks in organizations 16 Social Network Analysis: Cases and Concepts Mapping Networks: Tools
  17. 17. Social Network Analysis: Cases and Concepts http://www.dftdigest.com/images/Spyglass.jpg
  18. 18. Columbia IKNS Residency April 2016 The Business Case 18 Management Practice Business Need Talent Management Finding the natural leaders in the organization Innovation Identify boundary crossers Ensure organization has access to new ideas Collaboration Finding gaps in knowledge flow within groups, or across organizations or geographies Monitor or measure changes Knowledge management Identify and retain vital expertise Monitor or measure changes in k. exchange Organizational Change and Development Identifying opinion leaders for change management initiatives or during integration following mergers and acquisitions Organizational Performance Diagnosing cohesion among team members and targeting critical connections for improvement
  19. 19. Columbia IKNS Residency April 2016 Rob Cross’s Classic Case: A Performance Issue 19 From: The Organizational Network Fieldbook, Rob Cross et al, Jossey-Bass 2010 Where are the most frequent information flows? Formal Structure Informal Structure
  20. 20. Columbia IKNS Residency April 2016 A Classic Case 20 From: The Organizational Network Fieldbook, Rob Cross et al, Jossey-Bass 2010 Formal Structure Informal Structure
  21. 21. Columbia IKNS Residency April 2016 A Classic Case From: The Hidden Power of Social Networks, Rob Cross and Andrew Parker, Harvard Business School Press, 2004 21 From: The Organizational Network Fieldbook, Rob Cross et al, Jossey-Bass 2010 Formal Structure Informal Structure
  22. 22. Columbia IKNS Residency April 2016 A Classic Case 22 From: The Organizational Network Fieldbook, Rob Cross et al, Jossey-Bass 2010 Formal Structure Informal Structure
  23. 23. Columbia IKNS Residency April 2016 A Classic Case 23 From: The Organizational Network Fieldbook, Rob Cross et al, Jossey-Bass 2010 Formal Structure Informal Structure
  24. 24. Columbia IKNS Residency April 2016 What Factors Influence Connections? • Homophily: Birds of a feather, flock together • Propinquity: Those close by, form a tie 24
  25. 25. Columbia IKNS Residency April 2016 Elements in a Network Diagram 25 • A network diagram shows a collection of entities (nodes) linked by a type of relationship (represented by an edge) Nodes Edges Node: Vertex, Alter Edge: Tie, connection, link Network diagram: graph, sociogram Synonyms
  26. 26. Columbia IKNS Residency April 2016 Nodes Have Attributes • Information from survey and/or HR data*: – Organizational unit – Job title/role – Location – Expertise – Job level – Age – Gender • Additional attributes may come from the survey data itself 26 *within the bounds of what is legal and appropriate
  27. 27. Columbia IKNS Residency April 2016 About Edges 27 • Edges (and the graph as a whole) are either: • Undirected (merely connected) • Directed (edges go “from-to”) • Reciprocity sometimes matters Undirected Node: Vertex, Alter Edge: Tie, connection, link Network diagram: graph, sociogram Synonyms Directed Reciprocal
  28. 28. Columbia IKNS Residency April 2016 Edges Define the Shape of the Network 28 • In a survey we might ask: • “I get information from this person” • “I socialize with this person” • “I think this person is an expert” • “I go to this person when I have an idea I want to explore” • In looking at data, we might want to find out: • People who responded to each others’ emails • People who attended the same meetings or who appeared at the same event – or in the same scene! In creating a social network diagram, we define what we mean by an edge
  29. 29. Columbia IKNS Residency April 2016 Weights and Tie Strength 29 • Edges may have values, or weights, associated with them. For example the difference between: • Exchanging a few emails • Being best friends • The strength between two nodes may also reflected having multiple relationships: • Exchange information frequently AND • Socialize AND • Share trusted information Node: Vertex, Alter Edge: Tie, connection, link Network diagram: graph, sociogram Synonyms
  30. 30. Columbia IKNS Residency April 2016 Edge Data from Surveys 30 • Surveys: – Edge data may or may not be weighted – People may answer questions about everyone in the network or nominate people they communicate with, seek advice from, etc. • Weighted questions may denote frequency or some kind of strength
  31. 31. Cases http://www.dftdigest.com/images/Spyglass.jpg
  32. 32. Columbia IKNS Residency April 2016 How Are We Managing Expertise? Acknowledged Expert Colleague Questions visualized on the map: 1. Whom do you turn to for professional advice regarding your daily work? 2. Who is the most acknowledged professional in your field? Source: Maven7/Orgmapper
  33. 33. Columbia IKNS Residency April 2016 How Are We Managing Expertise? Accessible knowledgeAcknowledged Expert Colleague Group with no direct access to a knowledge center Questions visualized on the map: 1. Whom do you turn to for professional advice regarding your daily work? 2. Who is the most acknowledged professional in your field? Non-accessible knowledge Source: Maven7/Orgmapper
  34. 34. Columbia IKNS Residency April 2016 How Are We Managing Expertise? Acknowledged Expert Colleague Cluster with no direct access to a knowledge center Questions visualized on the map: 1. Whom do you turn to for professional advice regarding your daily work? 2. Who is the most acknowledged professional in your field? Source: Maven7/Orgmapper
  35. 35. Columbia IKNS Residency April 2016 California Computer 35 From “Informal Networks: The Company” David Krackhardt and Jeffrey R. Hanson HBR, 1993 CEO Leers must choose someone to lead a strategic task force. Bair Stewart Ruiz O'Hara S/W Applications Harris Benson Fleming Church Martin Lee Wilson Swinney Huberman Fiola Calder Field Design Muller Jules Baker Daven Thomas Zanados Lang ICT Huttle Atkins Kibler Stern Data Control Leers CEO
  36. 36. Columbia IKNS Residency April 2016 California Computer 36 From “Informal Networks: The Company” David Krackhardt and Jeffrey R. Hanson HBR, 1993 CEO Leers must choose someone to lead a strategic task force. Bair Stewart Ruiz O'Hara S/W Applications Harris Benson Fleming Church Martin Lee Wilson Swinney Huberman Fiola Calder Field Design Muller Jules Baker Daven Thomas Zanados Lang ICT Huttle Atkins Kibler Stern Data Control Leers CEO
  37. 37. Columbia IKNS Residency April 2016 Was Harris a Good Choice? 37 Whom do you go to for help or advice? Field Design Data Control Systems Software Applications CEO ICT
  38. 38. Columbia IKNS Residency April 2016 Was Harris a Good Choice? 38 Whom do you go to for help or advice? Field Design Data Control Systems Software Applications CEO ICT
  39. 39. Columbia IKNS Residency April 2016 The Question of Trust 39 Whom would you trust to keep in confidence your concerns about a work- related issue?
  40. 40. Columbia IKNS Residency April 2016 The Question of Trust 40 Whom would you trust to keep in confidence your concerns about a work- related issue?
  41. 41. Columbia IKNS Residency April 2016 The Question of Trust 41 Whom would you trust to keep in confidence your concerns about a work- related issue?
  42. 42. Columbia IKNS Residency April 2016 Network Patterns Multi-Hub Clustered Core/Periphery 42 Hub and Spoke
  43. 43. Columbia IKNS Residency April 2016 Core/Periphery 43 Core Periphery Structural Hole Isolates
  44. 44. Columbia IKNS Residency April 2013 It’s all about Questions 44 Patterns provide insights that provoke good questions. Full stop.
  45. 45. Columbia IKNS Residency April 2016 • Look at the whole network and its components Network Analysis Also Provides Metrics • Look at positions of individuals in the network Centrality Metrics Structural (Network) Metrics 45
  46. 46. Columbia IKNS Residency April 2016 Structural Metrics 46 • Common measures: –Density of interactions –Distance (average degree of separation) –Diversity –Communities, or groups –Centralization • Good for comparing questions, groups within networks or for comparing changes in a network over time Look at the whole network and its components
  47. 47. Columbia IKNS Residency April 2016 The Metrics: Density 47 Density. Data provides the percentage of information-getting relationships that exist out of the possible number that could exist. It is not a goal to have 100%, but to target the junctures where improved collaboration could have a business benefit. Percent of connections that exist out of the total possible Low Density High Density
  48. 48. Columbia IKNS Residency April 2016 Impact on Business of Connectivity • Bank management was trying to understand the differences across branches in sales at credit and deposit figures • Using network analysis, the bank was able to understand where to direct mentoring and “best practice” exchanges across banks 48 Figures show the performance differences in bank branches based on the density of their relationships Total credit / person Total deposit / person Low density branches High density branches Low density branches High density branches Source: Maven7/Orgmapper
  49. 49. Columbia IKNS Residency April 2016 Metrics help reveal diversity within networks SmA Ops PL A PL B PL C LgA 10 5 8 8 9 10 Small Accounts 72% 2% 11% 0% 2% 5% Operations 4% 85% 10% 5% 7% 12% Product Line A 8% 3% 77% 0% 1% 4% Product Line B 0% 13% 2% 73% 0% 17% Product Line C 2% 16% 1% 3% 54% 17% Large Accounts 2% 18% 5% 16% 12% 73% Density. Data provides the percentage of information-getting relationships that exist out of the possible number that could exist. It is not a goal to have 100%, but to target the junctures where improved collaboration could have a business benefit.  The diagonal shows the interconnectivity among groups in the organization  Off-diagonal, the metrics illustrate the extent to which people are reaching across organizational boundaries 49
  50. 50. Columbia IKNS Residency April 2016 Tracking Metrics Over Time 50 2010 2011 Year # Density Degree 2009 55 2.2% 1.2 2010 90 2.7% 2.4 2011 85 5.3% 4.5 2012 82 8% 6.88 2009 2012
  51. 51. Columbia IKNS Residency April 2016 Structural Metrics: Distance 51 Maximum number of steps to get from one node to another: 12 Average number of steps: 5
  52. 52. Columbia IKNS Residency April 2016 Centrality Metrics: Degree 52Based on: https://plus.google.com/+DaveGray/posts/CQRVeKEsUvF Raw number of connections (undirected network) 6 7 10 Average Degree: 3.28
  53. 53. Columbia IKNS Residency April 2016 Centrality Metrics: In-Degree and Out-Degree 53Based on: https://plus.google.com/+DaveGray/posts/CQRVeKEsUvF Number of in-coming and out-going connections Outdegree = 7 Indegree = 5
  54. 54. Columbia IKNS Residency April 2016 Centrality Metrics: Betweenness 54Based on: https://plus.google.com/+DaveGray/posts/CQRVeKEsUvF How many paths does a single node lie on? 855 1080 785 793
  55. 55. Columbia IKNS Residency April 2016 Centrality Metrics: Betweenness Highest Bee-tweenness? https://www.timeshighereducation.com/sites/default/files/styles/the_breaking_news_image_style/public/bees_teamwork.jpg h/t: Valdis Krebs
  56. 56. Columbia IKNS Residency April 2016 Centrality Metrics: Closeness 56Based on: https://plus.google.com/+DaveGray/posts/CQRVeKEsUvF Able to reach all the other nodes in the fewest steps
  57. 57. Columbia IKNS Residency April 2016 Using Metrics: Finding Key Opinion Leaders 57 Source: Maven7
  58. 58. Columbia IKNS Residency April 2016 Using Metrics: Finding Key Opinion Leaders 58 Source: Maven7
  59. 59. Columbia IKNS Residency April 2016 Using Metrics: Finding Key Opinion Leaders 59 Dunbar’s number: 150 • Strong ties: – Close, frequent – Reciprocal – May be embedded in a strong “local network” • Weak ties – Infrequent interaction – Likely embedded in other (diverse) networks – Accessible as needed Source: Maven7
  60. 60. Columbia IKNS Residency April 2016 Centrality Metrics: Brokerage, Closure 60Based on: https://plus.google.com/+DaveGray/posts/CQRVeKEsUvF Working cross-cluster or within clusters?
  61. 61. Columbia IKNS Residency April 2016 Centrality Metric: Eigenvector 61 Connected to well-connected nodes
  62. 62. Columbia IKNS Residency April 2016 Putting Some Metrics Together 62 http://qz.com/650796/mathematicians-mapped-out-every-game-of-thrones-relationship-to-find-the-main-character/
  63. 63. Columbia IKNS Residency April 2016 Which Technology Scout is Most Successful? 63 It's Whom You Know Not What You Know: A Social Network Analysis Approach to Talent Management, Eoin Whelan, SSRN: http://ssrn.com/abstract=1694453 Technology Scout Connector Gatekeeper Group member
  64. 64. Columbia IKNS Residency April 2016 Using Metrics: Ego Networks and Diversity • Organization • Expertise • Age, Tenure 65 External/Internal Ratio: Proportion of an individual’s ties that are in the same demographic cohort as the individual node (“ego”). Ranges from +1 (all external) to -1 (all internal) AB’s E/I index: .308 DC’s E/I index: -.714 Can be derived from any demographic: • Social Ties • Geographic location • Hierarchical position
  65. 65. Columbia IKNS Residency April 2016 The Importance of Diversity People who live in the intersection of social worlds are at higher risk of having good ideas. – Ron Burt 66
  66. 66. Columbia IKNS Residency April 2016 Organizational Networks Summary 67 • The science of networks has brought insights into the structure of organizational networks • Organizational network analysis lets us map relationships to: • Identify patterns of connection, disconnection, and flows of knowledge and ideas • Understand the roles that individuals play and their potential for enhancing organizational effectiveness • Developing and sharing maps and metrics helps organizations to ask good questions and design targeted interventions • A map represents a moment in time; when maps are shared the relationships start to shift
  67. 67. Columbia IKNS Residency April 2016 Interventions: Net Work Ways to change patterns in networks Practices from the KM/OD Repertoire Create more connections Make introductions through meetings and webinars, face-to-face events (like knowledge fairs); implement social software or social network referral software; social network stimulation Increase the flow of knowledge Establish collaborative workspaces, install instant messaging systems, make existing knowledge bases more accessible and usable Discover connections Implement expertise location and/or; discovery systems; social software; social networking applications Decentralize Social software; blogs, wikis; shift knowledge to the edge Connect disconnected clusters Establish knowledge brokering roles; expand communication channels Create more trusted relationships Assign people to work on projects together Alter the behavior of individual nodes Create awareness of the impact of an individual’s place in a network; educate employees on personal knowledge networking Increase diversity Add nodes; connect and create networks; encourage people to bring knowledge in from their networks in the world 68
  68. 68. Mapping Networks: Tools http://quilting.about.com/od/picturesofquilts/ig/Alzheimer-s-Quilts/The-Ties-that-Bind.htm
  69. 69. Columbia IKNS Residency April 2016 What Sorts of Tools Are There? Category of Tool What you need to know Expert/Researcher Mapping and Analysis Tools Range in complexity of function and cost Emerging Platforms Network diagrams can be shared on the web Consulting Vendors Specialized solutions with project life cycle management Mapping social metadata Email and log file analysis Personal network assessment DIY or $$$
  70. 70. Columbia IKNS Residency April 2016 Expert/Research Tools …plus many more
  71. 71. Columbia IKNS Residency April 2016 Data Flow Analysis & Mapping Tools Maps Metrics Edge Data UCINET NetDraw InFlow NodeXL Collection Tools Spreadsheets Online Surveys Paper Node Data Social Media
  72. 72. Columbia IKNS Residency April 2016 ONASurveys • Specifically designed for doing network analysis • Demographic questions as well as network relationship questions • Users respond to network questions only about people they indicate they know • Outputs datasets for: – NetDraw/UCINET – NodeXL – Gephi 74
  73. 73. Columbia IKNS Residency April 2016 Tool Basics – the Dataset (0s and 1s) 75 Information about the nodes (vertices) and the ties (edges)
  74. 74. Columbia IKNS Residency April 2016 Node Attributes 76
  75. 75. Columbia IKNS Residency April 2016 Edges: Columns for Advice and Support 77
  76. 76. Columbia IKNS Residency April 2016 Open it Up … 78 What Attributes do We Want to use for the display?
  77. 77. Columbia IKNS Residency April 2016 Option … 79 Upload specific data when you create the NodeXL file
  78. 78. Columbia IKNS Residency April 2016 Size 80
  79. 79. Columbia IKNS Residency April 2016 Color 81
  80. 80. Columbia IKNS Residency April 2016 Short List of Resources for SNA/ONA Tools 82 http://tinyurl.com/SNA-ONA-Tools
  81. 81. Columbia IKNS Residency April 2016 Emerging Platforms: Kumu 83 https://www.kumu.io/explore
  82. 82. Columbia IKNS Residency April 2016 https://kumu.io/UnLtdUSA/austin-social-entrepreneurship Kumu is Based on Community 84
  83. 83. Columbia IKNS Residency April 2016 Emerging Platforms: Polinode • Create and manage surveys • Upload and manage networks 85 https://polinode.com/
  84. 84. Columbia IKNS Residency April 2016 Quick Comparison Feature/Capability Kumu Polinode Create and manage surveys No Yes; cost is based on # of survey respondents and # of names listed Metrics Yes Yes Control of colors, shapes, sizes & overall diagram GUI and CSS Stylesheets Via GUI and specializing attributes Publish maps on the web Yes Yes Share data and mapping Yes Yes Public network pricing Free • Free with basic metrics, up to 250 nodes and 1,000 edges • $20/month for advanced metrics and up to 50,000 nodes Private network pricing (per month) $23 (3 projects) $34 (5 projects) $49 (10 projects) $29 User community Yes Yes 86
  85. 85. Columbia IKNS Residency April 2016 Network Insights Don’t Require Fancy Software • If it’s a network, you can draw it. 87
  86. 86. Columbia IKNS Residency April 2016 Mapping from Social Media • Social network platforms: – A Facebook Friend – A LinkedIn Connection – A Twitter Following • Social media content platforms: – Likes, posts, replies, shares, and uploads – Mentions or “retweet” #hashtags • In-house: – Email 88
  87. 87. Columbia IKNS Residency April 2016 Twitter Networks in NodeXL: Patterns 89 Polarized Crowd Tight Crowd Brand Clusters Community Clusters Broadcast Networks Support Network http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
  88. 88. Columbia IKNS Residency April 2016 Networks in Social Media 1. Krugman tweets a link to an article 2. There are a number of Tweeters who publish links to the article but these are not connected to other Tweeters 3. There are two densely interconnected groups of people who share the link and discuss it 90 Analyzing Twitter networks with NodeXL: Broadcast Networks http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
  89. 89. Columbia IKNS Residency April 2016 Facebook from NodeXL 91
  90. 90. Columbia IKNS Residency April 2016 Swoop Analytics • Use interaction data to create and analyze edges in the network • External/internal ratios • Edges & reciprocal edges 92 Personal and Enterprise-level dashboards
  91. 91. Columbia IKNS Residency April 2016 SWOOP User Characterization • Using the metrics showing give/receive balance, SWOOP can provide feedback on typical user communication personas • Using overall metadata, SWOOP can provide benchmark information on an organization’s online collaboration engagement/ adoption 93 http://www.swoopanalytics.com/index.php/benchmarking/
  92. 92. CONSULTING VENDORS 9
  93. 93. Columbia IKNS Residency April 2016 Consulting Vendor Options Vendor If you are looking for… Working with Them Maven7 OrgMapper Complete project management of large scale (10,000’s employees) analysis for Change Management or Organizational Performance initiatives Licensing is per survey, based on # of participants and whether or not you are certified and doing the project with them in consultation. Syndio Social Change Management Talent Management Communications Impact Be their “customers for life” – bring in the tool, develop expertise and use it throughout the enterprise to manage large-scale change. DNA-7 Organizational Design Talent Management Leadership and Collaboration Projects are one-off at this point. Keynetiq A tool that provides 12 different survey templates, analytics, and interactive network maps with members’ profiles that employees can navigate and use to search for expertise. Monthly fee based on number of people in the company. Custom pricing for networks with more than 1000 employees. Also available ONA consulting, study design and coordination, and full ONA project management.
  94. 94. Columbia IKNS Residency April 2016 Maven7 OrgMapper • Methodology embedded in the analysis and mapping tools – Change management (Influence) – Organizational performance (Excellence) • Customizations managed through the consulting services 96 Customized surveys and reports
  95. 95. Columbia IKNS Residency April 2016 Syndio Social 97 Syndio Social Uses SNA to Build Management Dashboards 97 Highest social capital Most favorable to change
  96. 96. Columbia IKNS Residency April 2016 Keynetiq – Create a Survey 9
  97. 97. Columbia IKNS Residency April 2016 What to Consider in Selecting Tools • How often will you do this in-house? – If you want this to be an organizational competency, then you will want to designate one or more people to learn to use the tools – If you designate someone, will it be a data junkie (who will want the DIY tools) or an organizational expert with solid computer expertise? – If you want to do this on an occasional basis, then a consultant may be the right choice • How much flexibility do you need? – Do you want to run a range of metrics and dig into the data yourself or are you comfortable with using a standard set of metrics provided by a vendor? 99
  98. 98. Columbia IKNS Residency April 2016 Summary 100 • Social network analysis tools and methods are available to map organizational as well as your individual, personal network • The tools matter less than the network mindset – and the understanding that the structure of a network matters
  99. 99. Columbia IKNS Residency April 2016 http://about.me/pattianklam • 30 years in software engineering • 10 years in professional services knowledge management & methodology (Digital, Compaq, Nortel) • Independent consultant 14 years; thought leader in knowledge management and social network analysis • Charter member of Change Agents Worldwide 101

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