A high-level overview of social network analysis, providing background on how it came into the knowledge management field. Includes an example and core concepts pertinent to the audience, online community managers.

1. Finding the Patterns of Connection Patti Anklam The Community Roundtable, December 9, 2009 Social Network Analysis

3. Network Science Organizational Work 4 Practice

4. 1967: Six Degrees of Separation Omaha Boston Stanley Milgram, Yale University

5. The Science of Networks Roots in sociology/sociometry (Jacob Moreno, 1930s) Stanley Milgram’s work in 1967 inspired the phrase “six degrees of separation” Mathematicians convened around the topic in 1975 INSNA (International Network for Social Network Analysis) founded in 1978 – expanding the discipline to include sociologists, management specialists, anthropologists, and other disciplines Late 1980s and early 1990s, Karen Stephenson, Valdis Krebs, and Gerry Falkowski began doing research at IBM Late 1990s, Rob Cross, Andrew Parker, and Steve Borgatti developed a research program at IBM’s Institute for Knowledge Management Rob Cross’s book, The Hidden Power of Social Networks, was published in 2004.

6. 2009: Going Mainstream

10. To understand the flow, find out what patterns exist.

11. Create a conversation to understand what shows up to be working and what is not

12. Design interventions to create, reinforce, or change the patterns to guide change toward a desired outcome.I frequently or very frequently receive information from this person that I need to do my job. Question What we learned from KM

13. Patterns of Performance At work: High performers have better networks People with better networks stay in their jobs longer Network-savvy managers are more likely to be promoted People with higher social capital coordinate projectsmore effectively

14. Patterns of Well-being In life: People with strong networks have a better chance of full recovery from heart attacks We are defined by the networks we are in Obesity studies Smokers New York Times,, May 22, 2008

15. Patterns of Effectiveness Source: http://www.robcross.org/sna.htm

16. Scattered clusters Hub-and-Spoke Multi-hub Core Periphery Time Where most network-building begins Self-sustaining network Source of network maps: Valdis Krebs Patterns of Network Growth

17. Patterns in Connection Strong ties: Close, frequent Reciprocal Weak ties Infrequent interaction No emotional connection Absent ties No personal connection beyond “nodding” Dunbar’s number: 150

18. Organizational Networks:“The Office Chart that Really Counts”* *http://www.businessweek.com/magazine/content/06_09/b3973083.htm Map: MWH Global, Vic Gulas

19. SNA in Organizations (1999) …a targeted approach to improving collaboration and network connectivity where they yield greatest payoff for an organization – Rob Cross & Andrew Parker … a mathematical and visual analysis of relationships / flows / influence between people, groups, organizations, computers or other information/knowledge processing entities– Valdis Krebs

20. Here’s the case of the collaborative cabinet: Professional services firm reorganized three months prior, with a goal to enhance collaboration across Three product lines Two industry segments The executives “talked a good game” about collaboration But Sr. VP wasn’t seeing it Offsite meeting was planned to work on “improving collaboration”

22. Message from Sr. VP about importance of survey

23. Data collection:

25. Questions: The Art of an SNA Problem (Examples) Relationships of Interest Improve collaboration Finding key connectors in organizations and communities Leadership development Performance benchmarking Mergers and acquisitions Know-about Information flow Communication Energy Problem-solving Decision-making Sense-making … many more Shares new ideas with Knows expertise of Works closely with Seeks help for problem-solving

26. The Network Map I frequently or very frequently receive information from this person that I need to do my job. Function = Small Accounts = Large Accounts = Product Line A = Product Line B = Product Line C = Operations

27. …and show patterns of individual roles Patterns of Groups Network Measures Density = 15% Cohesion = 2.6 Centrality = 6

28. …and show patterns of individual roles Peripheral specialists Information broker Central connector Well-positioned Influencer Patterns of Individual Roles Structural Hole

29. Density analysis shows group-to-group patterns 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. Frequently or very frequently receive

30. Moving into Action Often the presentation of the results provides sufficient self-awareness for the group to move into action Typical actions fall into three broad categories: Make an organizational shift or adjustment: role change, role addition, relocation, etc. Increase the knowledge capacity of the organization: provide opportunities for people to meet, to find one another on the web, add blogs, etc. Focus on individual behaviors of key people to distribute knowledge sharing across the organization

31. Impact of this Analysis Project Organizational response: change the context Established new roles for liaison Clarified role of “single point of contact” Develop the networks of relationships Within groups: face-to-face Across groups: put people on teams together Establish cross-group presence at staff meetings Individual Reallocation of decision-making Private and public commitments to change behavior

33. Practice Points

34. Basic Steps Identify the business problem and the scope of the network Collect data about the relevant relationships Use computer analysis tools Validate the findings through interviews and workshops Design and implement interventions to change the network Follow up

36. most likely to influence and be influenced directly

37. Closeness

38. most likely to find out first

39. Betweenness

40. most likely to broker and synthesize diverse info

41. Eigenvector

42. most likely to influence and be in the knowSource: Bruce Hoppe

43. Analytics in Large Data Sets Coordinator- This person connects people within their group. Gatekeeper - This person is a buffer between their own group and outsiders. Influential in information entering the group Representative - This person conveys information from their group to outsiders. Influential in information sharing. Consultant – This person acts as a mediator between two people Liaison – This person connects two people in different groups

44. Maps Can Measure Progress Map: MWH Global Sep 2004 Map: MWH Global Aug 2003

45. Issues and Challenges Constructive interpretation Remembering that the network analysis doesn’t provide “truth” but that its primary value is to provoke really, really good questions Privacy and confidentiality Responsible consultants address these in the design and communications program that is part of an ONA. Results in many cases are shown only anonymously.

46. Emergence of Social Media Blogging (c. 1999) Communispace (1999) Friendster, Ryze (2002) LinkedIn, SpaceBook, Del.icio.us, (2003) Facebook (2004) Twitter (2006)

48. Data Gathering email mining is common in research environments apis are available for most social networking/ media apps “Easy” to gather data about who’s connected to whom Graphs of thousands and hundreds of thousands of people are possible

49. What is a relationship?

50. How do you measure “goodness”? Activity? Operational trends? Behavior change? Organizational outcomes? Social Capital Quantitative Qualitative

51. Summary The work of the next decade is to develop our capabilities in creating and managing network structures The science continues to advance our understanding We can use our knowledge of the structure of networks and theirproperties to better serve individual, organizational, and societal goals

53. http://www.pattianklam.com

54. http://www.twitter.com/panklam

55. http://www.theappgap.com/?author_name=panklamThank you. Question 40