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Network Analysis in Two Parts
(with an Introduction)
Patti Anklam
Columbia IKNS Unit 4
April 2016
Introduction: Graph Theory Put to Work
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
Columbia IKNS Residency April 2016
Symposium on Social Networks: Dartmouth, 1975
http://eclectic.ss.uci.edu/~drwhite/Networks/MSSB1975.html
Columbia IKNS Residency April 2016
2007
Network Theory Reaches the Business World
2002
2002
2002
2003
2004
2004
5
2005
2009
2009
2002
2002
Columbia IKNS Residency April 2016
Organizational Networks
6
Source: MWH Global, Vic Gulas
Columbia IKNS Residency April 2016
Disease and Health
7
Columbia IKNS Residency April 2016
Networks of Companies
8
Source: Laurie Lock Lee, http://www.optimice.com.au
Equipment Manufacturers
Systems integrators
Columbia IKNS Residency April 2016
https://kumu.io/UnLtdUSA/austin-social-entrepreneurship
People and Companies
9
Austin Social Entrepreneurship
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/
Columbia IKNS Residency April 2016
Showing Affiliations
11
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
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
I’ve become convinced that understanding
how networks work is an essential 21st
century literacy.
Howard Rheingold
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
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
Social Network Analysis: Cases and Concepts
http://www.dftdigest.com/images/Spyglass.jpg
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
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
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
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
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
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
Columbia IKNS Residency April 2016
What Factors Influence Connections?
• Homophily: Birds of a
feather, flock
together
• Propinquity: Those
close by, form a tie
24
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
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
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
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
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
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
Cases
http://www.dftdigest.com/images/Spyglass.jpg
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
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
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
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
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
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
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
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?
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?
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?
Columbia IKNS Residency April 2016
Network Patterns
Multi-Hub
Clustered Core/Periphery
42
Hub and Spoke
Columbia IKNS Residency April 2016
Core/Periphery
43
Core
Periphery
Structural
Hole
Isolates
Columbia IKNS Residency April 2013
It’s all about Questions
44
Patterns provide
insights that provoke
good questions.
Full stop.
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
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
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
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
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
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
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
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
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
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
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
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
Columbia IKNS Residency April 2016
Using Metrics: Finding Key Opinion Leaders
57
Source: Maven7
Columbia IKNS Residency April 2016
Using Metrics: Finding Key Opinion Leaders
58
Source: Maven7
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
Columbia IKNS Residency April 2016
Centrality Metrics: Brokerage, Closure
60Based on: https://plus.google.com/+DaveGray/posts/CQRVeKEsUvF
Working cross-cluster or within clusters?
Columbia IKNS Residency April 2016
Centrality Metric: Eigenvector
61
Connected to well-connected nodes
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/
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
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
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
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
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
Mapping Networks: Tools
http://quilting.about.com/od/picturesofquilts/ig/Alzheimer-s-Quilts/The-Ties-that-Bind.htm
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 $$$
Columbia IKNS Residency April 2016
Expert/Research Tools
…plus many more
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
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
Columbia IKNS Residency April 2016
Tool Basics – the Dataset (0s and 1s)
75
Information about the nodes (vertices) and the ties (edges)
Columbia IKNS Residency April 2016
Node Attributes
76
Columbia IKNS Residency April 2016
Edges: Columns for Advice and Support
77
Columbia IKNS Residency April 2016
Open it Up …
78
What Attributes do We Want to
use for the display?
Columbia IKNS Residency April 2016
Option …
79
Upload specific data when you create the NodeXL file
Columbia IKNS Residency April 2016
Size
80
Columbia IKNS Residency April 2016
Color
81
Columbia IKNS Residency April 2016
Short List of Resources for SNA/ONA Tools
82
http://tinyurl.com/SNA-ONA-Tools
Columbia IKNS Residency April 2016
Emerging Platforms: Kumu
83
https://www.kumu.io/explore
Columbia IKNS Residency April 2016
https://kumu.io/UnLtdUSA/austin-social-entrepreneurship
Kumu is Based on Community
84
Columbia IKNS Residency April 2016
Emerging Platforms: Polinode
• Create and
manage
surveys
• Upload and
manage
networks
85
https://polinode.com/
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
Columbia IKNS Residency April 2016
Network Insights Don’t Require Fancy Software
• If it’s a network, you can draw it.
87
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
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/
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/
Columbia IKNS Residency April 2016
Facebook from NodeXL
91
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
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/
CONSULTING VENDORS
9
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.
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
Columbia IKNS Residency April 2016
Syndio Social
97
Syndio Social Uses SNA to Build Management Dashboards
97
Highest social capital
Most favorable to change
Columbia IKNS Residency April 2016
Keynetiq – Create a Survey
9
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
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
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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Introduction to Social Network Analysis

  • 1. Network Analysis in Two Parts (with an Introduction) Patti Anklam Columbia IKNS Unit 4 April 2016
  • 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. Columbia IKNS Residency April 2016 Symposium on Social Networks: Dartmouth, 1975 http://eclectic.ss.uci.edu/~drwhite/Networks/MSSB1975.html
  • 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. Columbia IKNS Residency April 2016 Organizational Networks 6 Source: MWH Global, Vic Gulas
  • 7. Columbia IKNS Residency April 2016 Disease and Health 7
  • 8. Columbia IKNS Residency April 2016 Networks of Companies 8 Source: Laurie Lock Lee, http://www.optimice.com.au Equipment Manufacturers Systems integrators
  • 9. Columbia IKNS Residency April 2016 https://kumu.io/UnLtdUSA/austin-social-entrepreneurship People and Companies 9 Austin Social Entrepreneurship
  • 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. Columbia IKNS Residency April 2016 Showing Affiliations 11
  • 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. 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. I’ve become convinced that understanding how networks work is an essential 21st century literacy. Howard Rheingold
  • 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. 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. Social Network Analysis: Cases and Concepts http://www.dftdigest.com/images/Spyglass.jpg
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Columbia IKNS Residency April 2016 Network Patterns Multi-Hub Clustered Core/Periphery 42 Hub and Spoke
  • 43. Columbia IKNS Residency April 2016 Core/Periphery 43 Core Periphery Structural Hole Isolates
  • 44. Columbia IKNS Residency April 2013 It’s all about Questions 44 Patterns provide insights that provoke good questions. Full stop.
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Columbia IKNS Residency April 2016 Using Metrics: Finding Key Opinion Leaders 57 Source: Maven7
  • 58. Columbia IKNS Residency April 2016 Using Metrics: Finding Key Opinion Leaders 58 Source: Maven7
  • 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. 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. Columbia IKNS Residency April 2016 Centrality Metric: Eigenvector 61 Connected to well-connected nodes
  • 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. 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. 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. 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. 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. 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
  • 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. Columbia IKNS Residency April 2016 Expert/Research Tools …plus many more
  • 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. 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. Columbia IKNS Residency April 2016 Tool Basics – the Dataset (0s and 1s) 75 Information about the nodes (vertices) and the ties (edges)
  • 74. Columbia IKNS Residency April 2016 Node Attributes 76
  • 75. Columbia IKNS Residency April 2016 Edges: Columns for Advice and Support 77
  • 76. Columbia IKNS Residency April 2016 Open it Up … 78 What Attributes do We Want to use for the display?
  • 77. Columbia IKNS Residency April 2016 Option … 79 Upload specific data when you create the NodeXL file
  • 78. Columbia IKNS Residency April 2016 Size 80
  • 79. Columbia IKNS Residency April 2016 Color 81
  • 80. Columbia IKNS Residency April 2016 Short List of Resources for SNA/ONA Tools 82 http://tinyurl.com/SNA-ONA-Tools
  • 81. Columbia IKNS Residency April 2016 Emerging Platforms: Kumu 83 https://www.kumu.io/explore
  • 82. Columbia IKNS Residency April 2016 https://kumu.io/UnLtdUSA/austin-social-entrepreneurship Kumu is Based on Community 84
  • 83. Columbia IKNS Residency April 2016 Emerging Platforms: Polinode • Create and manage surveys • Upload and manage networks 85 https://polinode.com/
  • 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. Columbia IKNS Residency April 2016 Network Insights Don’t Require Fancy Software • If it’s a network, you can draw it. 87
  • 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. 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. 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. Columbia IKNS Residency April 2016 Facebook from NodeXL 91
  • 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. 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/
  • 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. 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. 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. Columbia IKNS Residency April 2016 Keynetiq – Create a Survey 9
  • 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. 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. 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