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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.

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Finding the Patterns of Connection

Patti Anklam The Community Roundtable, December 9, 2009

Social Network Analysis

About Me

• Knowledge management practitioner, then consultant

• Using enterprise collaboration tools since 1980

• Currently specializing in knowledge management, collaboration and social networks

• We live in networks all the time: communities, organizations, teams

• There is science to support the understanding of network structure

• The structure of a network provides insights into how the network “works”

• Once you understand the structure, you can make decisions about how to manage the network’s context: this is your “net work”

Net WorkThe Premise: Net Work

4

Organizational Work

Network Science

Practice

1967: Six Degrees of Separation

Stanley Milgram, Yale University

OmahaBoston

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.

2009: Going Mainstream

Question

• If it’s a network, you can draw it: -- People (Nodes) -- Relationships (Ties)

• Relationships can be analyzed:-- Counted, summed, averaged-- Grouped, segmented

• Illustrating: -- How separated the network is -- Who the central people are -- How connected the network is -- Who does the invisible work

• Patterns matter -- Demographics tell the story

What we learned from the science

QuestionWhat we learned from KM

• Work (knowledge, decisions, problem-solving, meaning) flows along existing pathways in organizations.

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

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

• 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.

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

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

Patterns of Effectiveness

Source: http://www.robcross.org/sna.htm

Source of network maps: Valdis Krebs

Scattered clusters Hub-and-Spoke Multi-hub Core Periphery

Time

Where most network-building begins

Self-sustaining network

Patterns of Network Growth

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

Organizational Networks:“The Office Chart that Really Counts”*

*http://www.businessweek.com/magazine/content/06_09/b3973083.htm Map: MWH Global, Vic Gulas

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

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”

The Sr. VP, his direct reports and all of their direct reports responded to a questionnaire

Preparation: Message from Sr.

VP about importance of survey

Data collection: Excel Spreadsheet

Questions: The Art of an SNA

• Improve collaboration• Finding key connectors in

organizations and communities• Leadership development• Performance benchmarking• Mergers and acquisitions• Knowledge in the retiring

workforce

Problem (Examples) Relationships of Interest

• Know-about• Information flow• Communication• Energy• Problem-solving• Decision-making• Sense-making• … many more

Shares new ideas with

Seeks help for problem-solvingWorks closely withKnows expertise of

Questions: The Art of an SNA

• Improve collaboration• Finding key connectors in

organizations and communities

• Leadership development• Performance benchmarking• Mergers and acquisitions

Problem (Examples) Relationships of Interest

• Know-about• Information flow• Communication• Energy• Problem-solving• Decision-making• Sense-making• … many more

Shares new ideas with

Seeks help for problem-solvingWorks closely withKnows expertise of

The Network Map

= Large Accounts= Small Accounts

= Product Line A

Function

= Product Line B= Product Line C= Operations

I frequently or very frequently receive information from this person that I need to do my job.

…and show patterns of individual rolesPatterns of Groups

Network MeasuresDensity = 15%Cohesion = 2.6Centrality = 6

Peripheral specialists

Influencer

Information broker

Central connector

Well-positioned

…and show patterns of individual rolesPatterns of Individual Roles

Structural Hole

Density analysis shows group-to-group patterns

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

SmA Ops PL A PL B PL C LgA10 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%

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

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

Practice Points

Basic Steps1. Identify the business

problem and the scope of the network

2. Collect data about the relevant relationships

3. Use computer analysis tools4. Validate the findings

through interviews and workshops

5. Design and implement interventions to change the network

6. Follow up

Positive Expected

Positive Unexpected

Negative Expected

Negative Unexpected

Using Metrics to Pinpoint Key Roles

Degree most likely to influence

and be influenced directly Closeness

most likely to find out first Betweenness

most likely to broker and synthesize diverse info

Eigenvector most likely to influence

and be in the know

Source: Bruce Hoppe

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

Maps Can Measure Progress

Map: MWH Global Aug 2003

Map: MWH Global Sep 2004

2003 2004Networks/Servers 19% 26%Messaging & Collaboration 31% 44%Application Support/Development 8% 11%End User Support (Help Desk) 25% 12%Field Operations 9% 10%Telecommunications 17% 50%Project Management 10% 13%Asset Management/Standards 38% 25%Other 20% 23%

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.

Emergence of Social Media

• Blogging (c. 1999)• Communispace (1999)• Friendster, Ryze (2002)• LinkedIn, SpaceBook,

Del.icio.us, (2003)• Facebook (2004)• Twitter (2006)

Social media in action (this week!) • DARPA’s Red Balloon Challenge -- Find the 10 balloons in the U.S. -- Identify by Latitude and Longitude -- In one day -- $40,000 prize

• MIT’s winning entry: -- Web site registration and broadcast -- Distributed prize money to finders and those who linked to them -- Details of how they mapped the links to the winners is yet to be disclosed

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

What is a relationship?

How do you measure “goodness”?

• Activity?• Operational trends?• Behavior change?• Organizational

outcomes?

Social CapitalQuantitative Qualitative

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 their properties to better serve individual, organizational, and societal goals

40

Question

• patti@pattianklam.com

• http://www.pattianklam.com

• http://www.twitter.com/panklam

• http://www.theappgap.com/?author_name=panklam

Thank you.

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